Artificial Intelligence (AI) is revolutionizing B2B ecommerce, enabling capabilities such as personalized product recommendations, dynamic pricing, and predictive analytics. However, the effectiveness of these AI-driven solutions depends heavily on the quality of the underlying data. Despite AI’s potential, poor data governance remains a significant challenge in the industry. A recent Statista survey revealed that 25% of B2B ecommerce companies in the United States have fully implemented AI technologies, while 56% are experimenting with them.

As AI adoption grows, B2B companies must address data quality issues to leverage AI’s benefits fully. Anyone who has spent time in the B2B industry will acknowledge that quality data is often a struggle. This article explores the critical importance of clean data in AI applications and offers strategies for improving data governance in the B2B ecommerce sector.

Common Symptoms of Bad Data Governance

Bad data governance is a pervasive issue in the B2B ecommerce landscape, particularly in industries like manufacturing, where complex supply chains and product catalogs create unique challenges. Here are some of the most common symptoms:

1. Duplicate Records: Customer and product data often contain duplicate entries due to inconsistent data entry processes or a lack of validation protocols. For example, a single customer might appear in the database multiple times with slight variations in name or contact information, leading to inefficiencies in communication and order processing.
2. Inconsistent Formatting: Manufacturing and distribution often involve extensive product catalogs, and inconsistencies in SKU formats, product descriptions, or units of measurement can disrupt operations. For instance, some entries might use “kg” while others use “kilograms,” confusing systems and causing inventory management and procurement errors.
3. Outdated or Missing Data: Stale data, such as outdated pricing, obsolete product details, or inactive customer accounts, can lead to misinformed decisions. Missing data, like incomplete shipping addresses or contact details, can result in delayed deliveries or lost opportunities.
4. Siloed Data Systems: Many B2B companies, especially in manufacturing, rely on disparate systems that don’t communicate effectively. A lack of integration between ERP systems, CRMs, and ecommerce platforms leads to fragmented data and manual reconciliation efforts, increasing the risk of errors.
5. Unreliable Vendor and Supplier Information: Manufacturing businesses often deal with a large network of suppliers, each with varying formats for invoices, contracts, and delivery schedules. Poorly managed supplier data can result in delayed production, stockouts, or overordering.

Why is Bad Data Governance So Prevalent in B2B Manufacturing?

Unlike B2C industries, where streamlined data processes are often a core focus, manufacturing businesses face unique challenges due to their operations’ complexity, reliance on legacy systems, and decentralized structures. Understanding why these problems are so prevalent is key to addressing the underlying causes and fostering long-term improvements.

1. Complexity of Operations: Manufacturing involves numerous moving parts—raw materials, suppliers, distributors, and customers—making data governance inherently more challenging. The sheer volume of data generated across the supply chain increases the likelihood of inconsistencies.
2. Legacy Systems: Many B2B manufacturing companies rely on outdated legacy systems not designed for modern ecommerce integration. These systems often lack robust data validation and cleaning mechanisms, perpetuating bad data practices.
3. Decentralized Operations: Manufacturing companies frequently operate in multiple locations, each with its own systems, processes, and data entry standards. This decentralization contributes to a lack of standardization across the organization.
4. Focus on Production Over Data: In traditional manufacturing mindsets, operational efficiency and production output take precedence over data accuracy. Thus, data governance investments may be considered a lower priority than equipment upgrades or workforce training.
5. Limited Awareness of the Impact: Many B2B organizations underestimate the long-term impact of bad data on their operations, customer satisfaction, and AI-driven initiatives. The focus often shifts to immediate problem-solving rather than addressing root causes through improved governance.

By recognizing these symptoms and understanding the reasons behind poor data governance, B2B manufacturing companies can take the first steps toward addressing these issues. This foundation is critical for leveraging AI and other technologies to their fullest potential in ecommerce.

Why Clean Data Governance is Non-Negotiable in the AI Era

AI thrives on data—structured, accurate, and relevant data. For B2B ecommerce, where AI powers everything from dynamic pricing to predictive inventory, clean data isn’t just a nice-to-have; it’s the foundation for success. Without clean data governance, AI systems struggle to provide reliable insights, leading to poor decisions and diminished trust in the technology.

As the B2B commerce world embraces AI, those who recognize and prioritize addressing a systemic industry problem of bad data will quickly move to the front of the pack. Garbage in, garbage out. Implementing AI tools with bad data will be doomed to failure as the tools will be ineffective. Meanwhile, those who take the time to ensure they have a good foundation for AI support will overtake the competition. It’s a watershed moment for the B2B industry where those who recognize how to get the most value out of AI while those who refuse to alter their own internal workflows because “that’s the way it’s always been done” will see their market share diminish.

1. Accuracy and Relevance: AI models rely on historical and real-time data to make predictions and recommendations. If the data is inaccurate or inconsistent, the AI outputs become unreliable, directly impacting decision-making and customer experiences.
2. Scalability and Growth: In an era where B2B companies are scaling rapidly to meet global demands, clean data ensures that AI systems can grow alongside the business. Bad data governance introduces bottlenecks, stifling the scalability of AI-driven solutions.
3. Customer Experience: AI-powered personalized recommendations, accurate delivery timelines, and responsive customer service are critical to building customer trust and loyalty. These benefits rely on clean, well-governed data. A single misstep, like recommending the wrong product or misquoting delivery times, can damage a company’s reputation.
4. AI Amplifies Data Issues: Unlike traditional systems, AI doesn’t just process data—it learns from it. Bad data doesn’t just result in poor outputs; it trains AI systems to make flawed assumptions over time, compounding errors and reducing the ROI of AI investments.
5. Competitive Advantage: Clean data governance can be a differentiator in a competitive B2B market. Companies with well-maintained data are better positioned to leverage AI for faster decision-making, improved customer service, and operational efficiencies, giving them a significant edge.

Ignoring data governance in the AI era isn’t just a missed opportunity—it’s a liability. Poor data practices lead to inefficient AI models, frustrated customers, and, ultimately, lost revenue. Moreover, as competitors invest in clean data and AI, companies with bad data governance risk falling irreparably behind.

Clean data governance is no longer optional; it’s a strategic imperative in the AI-driven B2B ecommerce landscape. By prioritizing data accuracy and consistency, companies can unlock AI’s full potential and position themselves for long-term success.

How B2B Companies Can Address Bad Data Governance

Tackling bad data governance is no small feat, but it’s a journey worth undertaking for B2B companies striving to unlock AI’s full potential. The solution involves strategic planning, technological investment, and cultural change. Here are actionable steps businesses can take to clean up their data and ensure it stays that way:

1. Conduct a Comprehensive Data Audit
2. Standardize the Data Entry Process
3. Implement Master Data Management (MDM)
4. Leverage Technology for Data Cleaning and Enrichment
5. Break Down Silos with Integration
6. Foster a Culture of Data Ownership
7. Commit to Continuous Improvement

The first step is conducting a thorough data audit—think of it as a spring cleaning for your databases. By identifying gaps, redundancies, and inaccuracies, businesses can reveal the full extent of their data issues. This process isn’t just about finding errors; it’s about creating a baseline understanding of the company’s data health. Regular audits prevent these issues from snowballing into more significant, costly problems.

Once the audit is complete, it’s time to set some ground rules. Standardizing data entry processes is critical for ensuring consistency. Clear guidelines for formatting SKUs, recording customer details, and storing supplier information can prevent the chaos of mismatched or incomplete records. Employees should be trained on these standards, and tools like automated forms or validation rules can make compliance seamless.

Of course, even the best data entry standards won’t help if different systems across the organization aren’t communicating. That’s where Master Data Management (MDM) comes in. By centralizing data into a single source of truth, companies ensure that updates in one system are automatically reflected across all others. With MDM in place, teams can work confidently, knowing that their data is accurate and consistent.

But standardizing and centralizing aren’t enough if you’re already sitting on a mountain of messy data. Performing this step by hand is significantly time-intensive. Enter data cleaning and enrichment tools. AI-powered solutions can quickly identify and correct errors, deduplicate records and fill in missing fields. These tools don’t just clean up the past; they automate routine processes to keep data clean moving forward.

For many B2B companies, fragmentation is one of the biggest hurdles to clean data. Silos between ERP systems, CRM platforms, and ecommerce tools create inconsistencies that ripple across the business. Breaking down these silos through system integration ensures a unified flow of data, improving collaboration and decision-making across departments. This requires a thoughtful integration strategy, often with the help of IT experts, but the payoff is well worth the effort.

Clean data isn’t just a technical problem—it’s a cultural one. Companies must foster a culture of data ownership, where employees understand the importance of the data they handle and feel accountable for its accuracy. Assigning clear responsibilities, such as appointing a Chief Data Officer (CDO) or similar role, can ensure that data governance remains a priority.

Finally, data governance isn’t a one-and-done project. Continuous improvement is essential. Regular review of data policies and feedback from team members help refine processes over time. Establishing KPIs for data quality can also provide measurable insights into the success of these efforts.

By taking these steps, B2B companies can move from reactive problem-solving to proactive data management. Clean, well-governed data isn’t just the backbone of AI success—it’s a strategic asset that drives better decisions, smoother operations, and stronger customer relationships. In an increasingly data-driven world, those who master their data will lead the way.

Conclusion: Turn Your Data into a Competitive Advantage in the AI Era

In the rapidly evolving landscape of B2B ecommerce, integrating AI technologies offers unprecedented opportunities for growth and efficiency. However, as we’ve explored, the effectiveness of AI is intrinsically linked to the quality of the underlying data. Companies risk undermining their AI initiatives without robust data governance, leading to inaccurate insights and missed opportunities.

Perficient stands at the forefront of addressing these challenges. With extensive experience in implementing comprehensive data governance frameworks, we empower B2B organizations to harness the full potential of their data. Our expertise encompasses:

• Product Information Management (PIM): We assist in managing all aspects of your product data—from SKUs and descriptions to stock levels and pricing—ensuring consistency and accuracy across all platforms.
• Digital Asset Management (DAM): Our solutions help organize and distribute digital assets related to your products, such as photos and videos, enhancing the efficiency of your operations.
• Data Integration and Standardization: We streamline your data processes, breaking down silos and ensuring seamless communication between systems, which is crucial for effective AI implementation.

Investing in clean data governance is not just a technical necessity but a strategic imperative. With Perficient’s expertise, you can transform your data into a powerful asset, driving informed decision-making and sustainable growth in the AI era.

Justin Racine, Principal of Unified Commerce Strategy, has written about some of the most gripping commerce and retail topics this year. In 2024, he’s decoded how Elon’s new Justin’s expertise and passion for cutting-edge technology transforming the commerce landscape brings great depth and nuance to his articles. In addition, his coverage of and poignant reflections on the conferences he attends sent his thought leadership to the top of featured article and must-read lists.  Therefore, it is no surprise that Justin has been named CMSWire’s Contributor of the Year for the third year in a row.

We sat down with Justin before he jets off for his next conference – NRF 2025 – to get some much-needed answers to the most asked commerce and retail questions we’ve received at Perficient.

What are the growing trends that you see shaping the future of ecommerce, and how are you helping brands to stay competitive in those areas?

Our entire world is going to become “phygital.” In other words, physical and digital experiences will continue to blend into a perfectly crafted shopping journey that delights and surprises customers. While the concept of phygital will start to take shape in the form of augmented reality, product visualizers, and digital twins – it’s more likely that brands will first start with expanding their presence into new ecommerce channels, like social media.

According to eMarketer – social commerce revenue will pass $100B in 2025, a staggering 22% growth path from their last modeling in 2023. While some social channels’ futures are unknown, like TikTok – many other channels continue to surge. You’ll start to see the entrance of product placement and buying opportunities within YouTube – and eventually other streaming platforms like Netflix will start to adopt the ability to shop within your favorite shows.

Additionally, we’ll start to see large brands build out customer community efforts to compete with smaller, scrappier niche brands. Consumers are human beings, and human beings love to interact with others who share similar mindsets, values, and traits. The onset of these new digital communities will spawn enhanced elements of anthropomorphism between customer and brand, helping to create elevated levels of connection and ultimately, will drive increased ecommerce revenues.

What is the trajectory of AI’s impact on hyper-personalized customer experiences that brands should incorporate into an effective unified commerce strategy?

The world of hyper personalization will start to become more humanized as well as more predictive. Assisted shopping and autonomous shopping would then be the next logical step. This is where AI will start to intimately begin understanding your buying patterns, preferences, and previous decisions. It will start ordering products for you without a given prompt.

From there, AI will start to move into something akin to ‘empathetic personalization’ – where AI can start to understand sentiment, tone, and emotion. AI models will recommend products or services that are based upon how we feel or act within that specific day or environment.

Eventually, we all will have our own personalized shopping AI buddies who will help us with everything. They’ll assist in everything from scheduling the kids’ summer activities to buying and booking travel for spring break. AI personal assistants will become the future way to do anything and everything.

How about when you incorporate the advancements in connected products and robotics as digital and physical retail environments are beginning to blend (phygital)?

An example might be that you’re walking down the street, and you see a pair of shoes that you absolutely love.  Thankfully you happen to be wearing your Meta Ray-Ban sunglasses that cue up a blended augmented view of the shoes, where you can see exactly where you can purchase them, and also what they would look like on your feet by simply looking down. That will be the future of retail.

Additionally we’re going to see retail become more connected and unified than ever before. Brands will invest to leverage a 360 view of customer activity to personalize across the channel continuum. Once in-store beacon technology starts to take off, retailers will be able to personalize the entire shopping experience based upon where a customer is standing in the store, what they previously browsed through on that store’s website, and what they previously purchased.

In previous blogs, we’ve covered conversational commerce and the anthropomorphism of brands. What strategies would you recommend for companies looking to humanize their approach without losing scale during peak shopping seasons?

Humanizing at scale really just comes down to understanding your consumer and knowing what type of humanized touchpoints will resonate the most. I talked a little about this above, but once all of us have our own personal shopping assistants, the world of commerce becomes much more centralized and unified.

Think of the times where you consciously recognized that you needed to go to the store to buy this or that. Now with personal shopping agents, they will be there to help build shopping lists, remind you of what you need to purchase, and at times, purchase without your intervention. Taking it a step further, AI will start to have visibility into elements like product stock status and availability – giving you options and nudging you to make a purchase so that you receive the products in time. For example, “Hi Justin, reminder, your calendar shows you have holiday party next Thursday. If you want to order your Santa costume and have it before then, please order today. Would you like me to place that order for you?” Thus, ensuring you get what you want, when you want it, and that’s the future that I foresee.

Remember, connectivity strategies are only as good as the channel in which you provide them. Be sure to understand what channels your customers are active in first, then determine which stories you want to tell – and always, ALWAYS do the best you can to track the results to offer up pivots and lessons learned.

How do you think shoppers will initially react to such cutting-edge technology transforming their shopping experience?

I’m interested to see how consumers react to the idea of AI technology knowing more about us than we know about ourselves. In my mind, it can go one of two ways. First, consumers could reject this idea and choose to push back against the onset of AI technology being so intimately tied to our every action, every move, and every purchase. Or alternatively and more likely in my mind, is that consumers will start to adopt AI technology willingly. They will become dependent on its insights, its understanding, and ultimately, its goal to give us the best possible cross-channel, unified experiences. Will we be as reliant on AI as we are on our smartphones? Most likely. Will AI become the dominant race on this planet and take over the world? Only time will tell.

Be on the lookout for Justin’s next article covering his attendance at NRF: Retail’s Big Show coming up in the beginning of January. We’ll have live coverage of the first few days of the conference, and if you’re planning on attending, be sure to schedule a meeting with him and the rest of our experts.

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Shoptalk held its first ever Fall conference in Chicago this past week and our very own Justin Racine, Principal of Unified Commerce, was present to take it all in. This year’s theme was 007, so Justin was on a reconnaissance mission to gain as much information on retail and commerce trends as possible. Here’s a debrief on his sources and the intel he was able to gather from them during his time at the show.

Infiltrate the Indie Brand Mindset

Before the explosion of social media, consumers gained knowledge of new brands by window shopping and traditional ads, but the reach only went so far. With the prevalence of social media, retail products that are niche and unique now have access to the whole world with platforms like TikTok and Instagram.  Rent the Runway is a clothing rental brand that’s currently reveling in the fact that indie brands are controlling the fashion space. Jennifer Hyman, the Founder and CEO of Rent the Runway, claimed that today a brand can skyrocket in value just by a teenager talking about its products on TikTok. Social media has been changing the way consumers shop, and brands need to be able to keep up and provide new and fresh ways to connect and interact. Rent the Runway did that by providing fashion clothing for rent, rather than purchase, thus allowing for an ever-changing wardrobe with plenty of variety. Retail – just like fashion – should take greater risks and be bolder.

Capture Customers with Community and Connection

Glossier CEO, Kyle Leahy, provided a thoughtful look at three C’s that should be focused on to provoke thoughtful, engaging, and relatable conversations between brands and their consumers. Community, connection, and customers are the three C’s in question here. Glossier prides itself on being a community-based brand, focusing on how their products make people feel. On stage, Kyle spoke about how their consumers come to them because of the community they’ve created, and by actively listening to their consumers.

However, it’s one thing just to listen – it’s another thing entirely to respond. Kyle stated that Glossier responds to every single comment or post on their socials. That’s how they’re building a strong community, like by like, comment by comment. Glossier doesn’t stop there; they continually strive for personalized customer experience. Their storefronts are full of local apparel and products, and a new fragrance they’ve recently launched is an aggressively impressive campaign.

It’s called Glossier You, and the bottle is specifically designed to be activated by using the consumer’s thumb. In this way, it gives the feeling of the product being “encoded” to their thumbprint, giving them a personalized experience. To go one step further, they claim the smell is a little different on each person. With this product, they’ve built a conversation piece for their consumers around how it’s unique to them allowing connection across users for comparison, thus creating a shared experience and further solidifying their community.

Hacking Data to Delight Customers

Surprise and delight! That’s the name of the game when it comes to creating buzz with consumers. Many companies feel like they have a finger on the pulse, but according to the Vice President and Principal Analyst at Forrester, Brendan Witcher, that’s not the case. He challenged attendees’ perspectives saying that many customers don’t feel that companies are delighting or surprising them. These brands are operating off opinions rather than data, and these companies should gather real, credible data if they want to get to the heart of their consumers emotional responses.

Creative decisions should be based on true data and gained by letting the customer express themselves through their behaviors, actions, and even more importantly, their inactions. A great example of inactivity is those would be consumers who are visiting your site but not purchasing. Their lack of purchase, or inaction, is a clear look into a way to create growth simply by focusing on making those conversions. These customers are there, now all that’s needed is a push in the right direction.  Doubling up from Glossier’s 3C’s, Brendan revealed that there are 6 elements of customer data to focus on: characteristics, considerations, curiosities, conditions, context, and conceptions.

Closing the Dossier on Shoptalk Fall

For it being their first Shoptalk Fall, the show undoubtedly inspired and renewed the energy and enthusiasm of those in attendance. Individuals present expressed an urge to build deeper connections with their customers through a wide variety of strategies. The speakers encouraged the audience to listen to their customers, stay true to relevant retail trends, and dive deep into the data to curate connections and build communities. Now is the time to be unconventional, unique, and exciting.

Your mission, if you choose to accept it, is to explore Perficient’s industry expertise in retail and commerce.

Read Justin’s full article on CMSWire.

Recently, the news broke that Optimizely acquired Netspring, a warehouse-native analytics platform.

I’ll admit, I hadn’t heard of Netspring before, but after taking a closer look at their website and capabilities, it became clear why Optimizely made this strategic move.

Simplifying Omnichannel Analytics for Real Digital Impact

Netspring is not just another analytics platform. It is focused on making warehouse-native analytics accessible to organizations of all sizes. As businesses gather more data than ever before from multiple sources – CRM, ERP, commerce, marketing automation, offline/retail – managing and analyzing that data in a cohesive way is a major challenge. Netspring simplifies this by enabling businesses to conduct meaningful analytics directly from their data warehouse, eliminating data duplication and ensuring a single source of truth.

By bringing Netspring into the fold, Optimizely has future-proofed its ability to leverage big data for experimentation, personalization, and analytics reporting across the entire Optimizely One platform.

Why Optimizely Acquired Netspring

Netspring brings significant capabilities that make it a best-in-class tool for warehouse-native analytics.

With Netspring, businesses can:

• Run Product Analytics: Understand how users engage with specific products.
• Analyze Customer Journeys: Dive deep into the entire customer journey, across all touchpoints.
• Access Business Intelligence: Easily query key business metrics without needing advanced technical expertise or risking data inconsistency.

This acquisition means that data teams can now query and analyze information directly in the data warehouse, ensuring there’s no need for data duplication or exporting data to third-party platforms. This is especially valuable for large organizations that require data consistency and accuracy.

Ready to capitalize on these new features? Contact Perficient for a complimentary assessment!

The Growing Importance of Omnichannel Analytics

It’s no secret that businesses today are moving away from single analytics platforms. Instead, they are combining data from a wide range of sources to get a holistic view of their performance. It’s not uncommon to see businesses using a combination of tools like Snowflake, Google BigQuery, Salesforce, Microsoft Dynamics, Qualtrics, Google Analytics, and Adobe Analytics.
How?

These tools allow organizations to consolidate and analyze performance metrics across their entire omnichannel ecosystem. The need to clearly measure customer journeys, marketing campaigns, and sales outcomes across both online and offline channels has never been greater. This is where warehouse-native analytics, like Netspring, come into play.

Why You Need an Omnichannel Approach to Analytics & Reporting

Today’s businesses are increasingly reliant on omnichannel analytics to drive insights. Some common tools and approaches include:

• Customer Data Platforms (CDPs): These platforms collect and unify customer data from multiple sources, providing businesses with a comprehensive view of customer interactions across all touchpoints.
• Marketing Analytics Tools: These tools help companies measure the effectiveness of their marketing campaigns across digital, social, and offline channels. They ensure you have a real-time view of campaign performance, enabling better decision-making.
• ETL Tools (Extract, Transform, Load): ETL tools are critical for moving data from various systems into a data warehouse, where it can be analyzed as a single, cohesive dataset.

The combination of these tools allows businesses to pull all relevant data into a central location, giving marketing and data teams a 360-degree view of customer behavior. This not only maximizes the return on investment (ROI) of marketing efforts but also provides greater insights for decision-making.

Navigating the Challenges of Omnichannel Analytics

While access to vast amounts of data is a powerful asset, it can be overwhelming. Too much data can lead to confusion, inconsistency, and difficulties in deriving actionable insights. This is where Netspring shines – its ability to work within an organization’s existing data warehouse provides a clear, simplified way for teams to view and analyze data in one place, without needing to be data experts. By centralizing data, businesses can more easily comply with data governance policies, security standards, and privacy regulations, ensuring they meet internal and external data handling requirements.

AI’s Role in Omnichannel Analytics

Artificial intelligence (AI) plays a pivotal role in this vision. AI can help uncover trends, patterns, and customer segmentation opportunities that might otherwise go unnoticed. By understanding omnichannel analytics across websites, mobile apps, sales teams, customer service interactions, and even offline retail stores, AI offers deeper insights into customer behavior and preferences.

This level of advanced reporting enables organizations to accurately measure the impact of their marketing, sales, and product development efforts without relying on complex SQL queries or data teams. It simplifies the process, making data-driven decisions more accessible.

Additionally, we’re looking forward to learning how Optimizely plans to leverage Opal, their smart AI assistant, in conjunction with the Netspring integration. With Opal’s capabilities, there’s potential to further enhance data analysis, providing even more powerful insights across the entire Optimizely platform.

What’s Next for Netspring and Optimizely?

Right now, Netspring’s analytics and reporting capabilities are primarily available for Optimizely’s experimentation and personalization tools. However, it’s easy to envision these features expanding to include content analytics, commerce insights, and deeper customer segmentation capabilities. As these tools evolve, companies will have even more ways to leverage the power of big data.

A Very Smart Move by Optimizely

Incorporating Netspring into the Optimizely One platform is a clear signal that Optimizely is committed to building a future-proof analytics and optimization platform. With this acquisition, they are well-positioned to help companies leverage omnichannel analytics to drive business results.

At Perficient, an Optimizely Premier Platinum Partner, we’re already working with many organizations to develop these types of advanced analytics strategies. We specialize in big data analytics, data science, business intelligence, and artificial intelligence (AI), and we see firsthand the value that comprehensive data solutions provide. Netspring’s capabilities align perfectly with the needs of organizations looking to drive growth and gain deeper insights through a single source of truth.

Ready to leverage omnichannel analytics with Optimizely?

Start with a complimentary assessment to receive tailored insights from our experienced professionals.

Connect with a Perficient expert today!
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As we dive into the amazing capabilities of Optimizely One, let’s shine a spotlight on the Optimizely Data Platform (ODP). This simple tool unifies all your customer data in one place, making segmentation and personalization a breeze for your marketing team. With ODP, you’ll gain a complete view of your customers’ interactions and behaviors, empowering you to create personalized experiences, optimize your email marketing campaigns, and make smarter, data-driven decisions. Imagine effortlessly understanding your customers, delivering tailored experiences, and boosting your campaign performance – all while simplifying your data management. That’s the magic of ODP within Optimizely One!

Keep reading to discover how personalization with the Optimizely Data Platform (ODP) can enable simple and effective using real-time segmentation and AI generated customer insights.

Why Is A Customer Data Platform Important To Your Digital Business?

Imagine you’re remodeling your home. You might dream of a grand overhaul, but wisdom (and budget) suggests starting with one room to learn what works. This method is not only useful but also smart because it lets you meet the needs of your family and change things as needed without having to commit to a full renovation right away. This is the philosophy you can apply to personalizing digital experiences with customer data platforms (CDP).

There’s a lot of talk about CDPs and how valuable they are for personalization. You know it’s time to act because your customers want experiences that are tailored just for them. But diving into a full-scale CDP can feel like gutting your entire house when all you need is to repaint the living room. This is where the Optimizely Data Platform (ODP) comes into play, like a trusty set of tools that helps you piece together the customer data puzzle, one segment at a time.

With ODP, you’re laying down the hardwood floors of your digital strategy, creating a solid foundation for personalization and real-time segmentation. It’s about understanding each customer’s needs and behaviors before knocking down walls. By integrating ODP capabilities in tandem with other CDP solutions, you’re not just repainting; you’re reimagining spaces with the customer at the heart.

In this article, we’ll guide you through the flexibility of ODP, showing you how its versatility can help create customer journeys as unique as a custom-built home. You’ll see how segment by segment, ODP can enrich customer engagement, enable personalized experiences, and ultimately make sure your investment in data-driven marketing pays off, just like those home improvements that boost your property’s value.

So, let’s roll up our sleeves and walk through the considerations for using ODP. You’ll learn how to use it effectively, gain quick wins, and build towards a full-scale personalization that feels just like home.

Need Expert Help?  Contact Perficient for a complimentary assessment today!

Let’s untangle the web of questions and turn your data into actionable insights.

• Can I use ODP with other CDPs?
• How can we make the most of ODP?
• What are some example use cases that will show an ROI?

What’s ODP and CDP Got to Do With It?

As customers continue to demand more tailored experiences, businesses need to find ways to not only meet these expectations but also surpass them. Optimizely Data Platform (ODP) is a powerful tool that enables companies to achieve maximum personalization and real-time segmentation. ODP is a customer data platform (CDP) that enables businesses to collect, store, and manage customer data from multiple sources. This data can then be used to personalize customer experiences, allowing businesses to segment customers into precise cohorts.

Can I use ODP with other CDPs?

The short answer is Yes! Using Optimizely’s Data Platform (ODP) with other Customer Data Platforms (CDPs) can give businesses a comprehensive view of their customers. This data provides marketers with valuable insights, allowing them to create effective campaigns and more effectively target their audience. It also enables companies to track the performance of individual campaigns in real time, which gives them the opportunity to make necessary improvements or adjustments quickly.

ODP Plays Well With Others

Concerned about how ODP stacks up with other CDPs? Or you are unsure if you can also leverage your CRM, commerce or marketing automation tools. Rest easy. ODP is engineered to integrate seamlessly with powerhouse CDPs like Salesforce, Tealium, mParticle, and Segment. So, you can orchestrate your data with a robust CDP and then feed it into the ODP for refined segmentation and reporting. There are many out of the box integrations and if there is not a connector already built, Optimizely offers the ability to do custom integrations leveraging their framework of APIs.

Integrating ODP with another data sources like additional CRMs or marketing automation technology is very common for any business looking to maximize their marketing efforts and achieve maximum personalization. Doing so will give them access to a unified view of customer data from multiple sources, helping them capture complete customer profiles that are better tailored and more effective at engaging customers.

Example of AI-Generated Customer Insights within ODP 

How can we make the most of ODP?

Optimizely’s Data Platform (ODP) is a powerful tool that can help businesses personalize experiences, maximize performance, and gain valuable insights into customer behavior. Integrating ODP into the customer journey and CDP strategy allows businesses to collect as much data as possible, which can then be used to create tailored content in real time. Additionally, leveraging predictive analytics enables companies to anticipate potential customer actions and develop more targeted campaigns in the future. With all these capabilities, ODP has the potential to revolutionize how companies interact with their customers on a daily basis.

To truly capitalize on ODP, you need to see beyond the tech. It’s not just about deploying a tool; it’s about strategically aligning it with your growth roadmap.

What are some example use cases that will show a ROI?

Looking for tangible examples? Consider personalized landing pages that alter content based on visitor demographics or past behavior. Or, imagine a real-time segmentation model that instantly categorizes a user as a “DIY home repair enthusiast” based on their browsing history, offering them personalized product suggestions on the fly.

Optimizely’s Data Platform (ODP) can be a powerful tool to help businesses increase customer engagement and loyalty. By leveraging predictive analytics and real-time data, ODP allows companies to create tailored campaigns that are more likely to reach their intended audiences. Additionally, ODP enables companies to engage customers through targeted emails that offer incentives or discounts for completing purchases.

A/B testing is another way that businesses can utilize ODP to gain valuable insights into which versions of content perform best with different audiences or at different points in time. Finally, integrating ODP with popular analytics tools such as Google Analytics and Adobe Analytics gives companies the ability to quickly identify trends, measure performance against KPIs, and improve ROI from campaigns.

Let’s walk through some example tactics for personalization with Optimizely Data Platform (ODP):

Personalization with ODP from Advertising Campaigns

Creating digital marketing campaigns that are personalized based on the referring advertisement involves understanding your audience and strategies that help you tailor the user experience based on the information you have.

Here’s how you can do it:

1. Audience Segmentation – Start by dividing your audience into different segments based on their behaviors, demographics, interests, and source of referrals. This helps you understand the needs and preferences of different groups within your audience.

2. Personalized Calls-to-Action (CTAs) and Dynamic Content – Use dynamic content on your apps, website and landing pages. This allows the content to change based on the characteristics of the visitor, such as their source of referral. For instance, visitors coming from a social media ad for a particular product could land on a page where that product is highlighted. Create CTAs that are relevant to the referring advertisement. If your ad is about a specific offer, the CTA on your landing page could be “Save 20% On (This Offer) Now!”

3. Use UTM Parameters – UTM parameters in your ad URLs can track where your traffic is coming from and what campaign it’s associated with. You can then use this information to personalize the user journey.

By adopting these strategies, you can create a personalized user experience that aligns with the referring advertisement, thereby increasing the likelihood of achieving your campaign goals.

Creating Dedicated, Personalized Landing Pages

Personalized landing pages are specific web pages designed to deliver content that is tailored to the preferences, behaviors, and needs of individual visitors. Rather than presenting the same page to every visitor, a personalized landing page will dynamically adjust its content based on known data about the visitor.

This data can include:

1. Demographics and Geolocation – such as age, gender, location, and more.

2. Behavioral data – Info about the visitor’s past behavior, such as previous purchases, page visits, clicks, etc.

3. Referral source – Where the visitor came from, like a specific social media platform, search engine, or email campaign.

Personalized landing pages aim to increase engagement, relevance, and conversions because they offer a more directly relevant experience to the visitor. For example, if a visitor arrives at your site from an email campaign promoting a specific product, they might land on a page that offers more information specifically about that product rather than a generic homepage or overwhelming category page.

By catering to specific user needs and preferences, personalized landing pages can significantly improve conversion rates and reduce the average cost per action (CPA) for your advertising spend.

Real-Time Segmentation:

Real-time segmentation is a process that involves dynamically sorting customers into distinct groups or segments as they interact with your business in real time. It leverages live data and machine learning algorithms to identify patterns and behaviors, allowing businesses to personalize the customer experience instantaneously.

Here’s how real-time segmentation with ODP can make personalized experiences better:

1. Instant Personalization: Real-time segmentation allows you to provide personalized content, recommendations, or services to your customers immediately based on their current actions. For instance, if a customer is browsing DIY home improvement tools on your e-commerce site, real-time segmentation could categorize them into a “DIY enthusiast in Florida” segment, prompting the website to display more related products or home improvement tips.

2. Improved Customer Engagement: By understanding a customer’s needs and interests at the moment, you can engage them with relevant content and offers, thereby increasing their engagement and likelihood of making a purchase.

3. Timely and Relevant Interactions: Real-time segmentation allows businesses to send the right message at the right time. For example, a customer who abandons their shopping cart could be immediately targeted with a personalized reminder or offer to encourage them to complete their purchase.

In a world where customers expect personalized experiences, real-time segmentation is an invaluable tool. It not only enables businesses to meet these expectations but also helps them forge stronger connections with their customers, which can lead to increased customer retention and revenue.

Implementing ODP for maximum personalization and real-time segmentation

As our digital world continues to expand, so too does the complexity of marketing data. Embracing these tools can result in a significant increase in engagement, relevance, and conversions. But remember, the successful use of a CDP or ODP is contingent upon understanding its capabilities and using them to the fullest. Don’t let your data sit idle; let it work for you and transform your business.

Audiences Created in ODP Can Be Used Across Optimizely One Products

In order to maximize the potential of ODP for personalized campaigns and real-time segmentation, businesses must first identify the data sources they plan to apply. This includes collecting customer information from various sources such as web analytics tools, CRM systems, marketing automation platforms and commerce tools. Once all necessary data has been unified, companies can begin building targeted audiences based on specific characteristics or behaviors. If there is not a built-in integration for your CRM or data source, a custom integration can be created to meet your needs.

The next step is determining a strategy for creating custom campaigns with ODP that will reach customers in an engaging way. This entails deciding which channels are most effective (e.g., email, SMS or push notifications), setting up A/B tests for different versions of content and messaging, and scheduling initiatives ahead of time for maximum impact. Additionally, it is essential to understand customer preferences to deliver tailored content and messaging suited perfectly for each audience segment.

Personalization with Optimizely Data Platform (ODP) Can Be Simple and Effective

Organizations that are looking to maximize personalization and real-time segmentation should begin with a complimentary assessment for personalization. By scheduling a free consultation, businesses can gain tailored advice from experienced professionals.

Get in touch with a Perficient expert today!
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Retail Media and the Influencer Revolution

Change is the Norm

Retail leaders have always been adept at understanding and adapting to rapidly shifting markets. For example, the inception of Amazon and the global pandemic massively impacted the ways by which retail organizations operated. Moreover, retail leaders are gifted in the art of pivoting, capable of garnering internal buy-in for new initiatives that would have been unthinkable just five or ten years ago. Ultimately, change is the norm in retail, and an incredibly recent innovation and driver of change is the invention of retail media networks.

Retail Media Networks

According to eMarketer, a retail media network is an “advertising business run by a retailer that enables marketers to buy advertising space across the retailer’s owned and operated digital properties, as well as physical stores.”

This type of network represents a massive shift in thinking for the retail industry. For most in the industry, retail media networks represent a 15% increase in conversion rates and 66% of brands are shifting budget from other channels to retail media (Google, Amazon, BCG). Ultimately, this evolution of “sold shelf space” into the digital world is one of the most important revenue drivers in for modern retailers. As customers have embraced digital-first experiences, retailers that have positioned loyalty apps as the “digital shelf,” providing screen space to CPGs willing to pay or provide a deal to a shared targeted customer.

In addition to the clear financial upside, retail leaders no longer need to operate in isolation when generating interest, loyalty, and share of wallet. Instead, they can partner with manufacturers, complementary retailers, and the right media partners to drive purchases from the right consumers at the right time.

What does this have to do with Influencers?

The rise of social media has blurred the lines between passive and active brand engagement, and nowhere is this more relevant than in influencer marketing. An influencer or celebrity’s social media following represents a targeted, curated audience ready-made to drive interest. Moreover, the access of the audience to the influencer through social media often creates the type of relationship that the most effective brands want to generate. A wholehearted endorsement of a product by an influencer is often more than enough social proof to generate significant upticks in eCommerce purchases.

The big unlock yet to be fully discovered by retailers is this: Retail media networks, when integrated correctly, are a primary vehicle to deliver influencer messaging. Consider these examples:

1. A takeover of your favorite grocery app the Wednesday before Halloween by Wednesday Addams (of Netflix’s “Wednesday” fame) promoting a special pumpkin deal.
2. An eCommerce promoted outfit with a special “Kim’s take” caption that highlights a Kardashian’s opinion on the ensemble, as well as the right bundled product (also available on the site or for half-off in-store).
3. A website with an Aaron Judge promoted baseball bat that includes embedded videos of him hitting home runs off the bat as well as one of the leading competitors (i.e. a comparison)

This unique value proposition, once only accessible to the richest media agencies with the largest budgets, is now a potential enormous creative value driver. With the right strategy, it can differentiate retailers who may be struggling to find effective ways to reach new buyers.

How does any of this relate to AI?

More than any other initiative in retail that came before it, the adoption of retail media means an additional ocean of data is about to pour into retail organizations. The ability to predict the right celebrity or influencer to partner with that aligns with the retail brand is non-trivial. Furthermore, forecasting the results of any given retail media campaign is difficult but necessary to quantify. Experimentation, data-driven optimization, and AI-driven analyses are critical, as betting big on a particular retail media campaign should (and can) be done with all the predictive indicators aligning on that effort.

Artificial Intelligence is the best answer we have to maximize return on retail media efforts, as the number of ever-changing variables and the dynamic nature of retail media can create a perfect storm of unknown or hidden variables. In other words, injecting data-driven rigor into retail media efforts and building predictive models to optimize outcomes is imperative to select the best influencers with whom to partner, and the right offers and incentives to drive maximum ROI.

There will always be unknown variables in this ever-changing space, but leveraging the correct AI models can help retailers:

1. Identify the most suitable influencers for their brand and target audience
2. Predict the potential impact of influencer partnerships on sales and brand awareness
3. Optimize content and messaging for maximum engagement
4. Analyze real-time data to adjust campaigns on the fly
5. Measure and report on the ROI of influencer marketing initiatives

In conclusion, the convergence of retail media, influencer marketing, and AI presents an unprecedented opportunity for retailers to revolutionize their marketing strategies. By embracing these technologies and approaches, retailers can create more personalized, engaging, and effective campaigns that resonate with their target audience and drive tangible business results. As we move further into this new era of retail marketing, those who can effectively harness the power of AI to optimize their retail media and influencer partnerships will likely emerge as industry leaders.

The Future of Retail Media and Influencer Marketing

As we look ahead, several trends are likely to shape the future of retail media and influencer marketing:

1. Hyper-personalization: AI will enable retailers to create highly personalized experiences for each customer, tailoring influencer partnerships and product recommendations based on individual preferences and behaviors.
2. Micro and nano-influencers: While celebrity endorsements will remain valuable, there will be a growing focus on partnering with micro and nano-influencers who have smaller but highly engaged audiences in specific niches.
3. Augmented Reality (AR) integration: Influencers will increasingly use AR technology to showcase products in immersive ways, allowing customers to virtually try on clothes or visualize furniture in their homes.
4. Real-time campaign optimization: AI-powered tools will enable retailers to adjust their influencer campaigns in real-time based on performance data, maximizing ROI and engagement.
5. Cross-channel integration: Retail media networks will become more sophisticated in integrating influencer content across multiple channels, creating seamless customer experiences from social media to in-store displays.

Challenges and Considerations

While the potential of AI-driven retail media and influencer marketing is enormous, retailers must also be aware of potential challenges:

1. 1. Data privacy concerns: As retailers collect more data to power their AI models, they must ensure compliance with data protection regulations and maintain customer trust.
   2. Authenticity and transparency: As influencer marketing becomes more prevalent, maintaining authenticity and clearly disclosing sponsored content will be crucial to preserving consumer trust.
   3. AI bias: Retailers must be vigilant in monitoring and addressing potential biases in their AI models to ensure fair and inclusive marketing practices.
   4. Keeping pace with technological advancements: The rapid evolution of AI and marketing technologies will require ongoing investment in training and infrastructure.

Conclusion

The influencer revolution in retail, powered by AI and retail media networks, represents a paradigm shift in how brands connect with consumers. By leveraging these technologies, retailers can create more engaging, personalized, and effective marketing campaigns that drive real business results.

As the industry moves forward, the most successful retailers will be those who can effectively blend the art of influencer partnerships with the science of AI-driven optimization. They will create seamless, omni-channel experiences that resonate with consumers on a personal level, while continuously adapting to the ever-changing retail landscape.

The age of AI in retail media and influencer marketing is just beginning, and the possibilities are boundless for those willing to embrace this new frontier. As retail leaders continue to innovate and push boundaries, we can expect to see even more exciting developments in the years to come, forever changing the way brands and consumers interact in the retail space.

Personalization in Retail

As retailers and CPGs continue to innovate in the age of AI, customers increasingly expect personalized and relevant experiences at every interaction. According to the Salesforce 2020 State of the Connected Customer Report, customers are, on average, 70% more loyal to brands that offer personalized in-store and digital experiences compared to their competitors. Furthermore, brands that personalize experiences see a 23% higher conversion rate (BCG, 2019). These statistics clearly demonstrate that customer acquisition, retention, and satisfaction are intimately tied to the customer journey.
But how can an organization gain insights into that journey? How can retail brands possibly sift through the massive number of interactions to discover the moments ripe for personalization? And to what extent do personalization and product innovation need to partner to drive outcomes?

The answer is simple: Listen to your customers. Social media is a gift to brands, as customers often vent frustrations or share ideas about products and offerings on these platforms. With 66% of all customers expecting companies to understand their unique needs and expectations, the clear imperative is for brands to hear pain points on social media and adapt to feedback.

Listening to Customers

Ultimately, listening to feedback at scale requires Social Listening, a unique strategy and solution that allows for insights and monitoring of social media data signals for brand interactions. Using this approach, brands across the globe leverage strategies and tools to collect, filter, and search through brand mentions, contextualizing brand mentions. This allows brands to best understand how to serve their customers and deliver more compelling reasons to buy. Working with the largest brands on social listening platforms, we often see that using AI to infer sentiment, loyalty, and propensity to buy results in more loyal and satisfied customers.
As I have written before, our Journey Science framework is the most effective way to implement such data-led strategies. By combining social listening with advanced analytics and AI-driven insights, brands can create truly personalized experiences that resonate with their customers, driving both loyalty and business growth.

In conclusion, as we navigate the age of AI in retail, the key to success lies in understanding and responding to customer needs through personalization. By leveraging social listening and advanced analytics, brands can stay ahead of the curve and deliver the experiences that modern consumers demand.

With the 2024 Paris Summer Olympics now behind us, I pause for a moment to reflect on a time when the last summer games were held in Europe. The year was 2012, and the Olympics had just wrapped up in London, the queen had celebrated 60 years upon the throne, and in true royal fashion, I had just purchased the latest Ford Explorer.  

This Ford Explorer came in Triple Black with every feature including the latest version of sync with voice control. I was giddy with excitement and felt like I was Captain Kirk at the helm of the Starship Enterprise steering towards new horizons. But… the voice activation was not what I had hoped for.  

When attempting to call my mother, I got my friend Monica, and when trying to dial a colleague, I received a childhood friend. If you know me, then you understand that navigation isn’t my strong suit, and when searching for directions to Birmingham in Michigan, I would consequently be sent to Alabama. You get the picture.  

Speed back to 2024 and voice-to-everything is transforming the automotive industry. Thankfully, the voice control in my 2023 Ford Edge is now working much better — the way it was intended. 

Voice-to-Everything Technology Allows for Expanded Vehicle Control  

The automotive industry is undergoing a significant transformation driven by advancements in technology that are reshaping the way we interact with our vehicles. One of the most exciting developments in this space is the rise of voice-to-everything (VTE) technology. This innovation is poised to redefine the driving experience, increasing intuition, safety, and making it more connected than ever before.  

VTE technology refers to the integration of voice-controlled systems throughout a vehicle, allowing drivers and passengers to interact with the car’s functions using simple voice commands. This technology leverages advancements in artificial intelligence (AI) and natural language processing (NLP) to understand and execute spoken instructions, minimizing the need for physical controls or manual inputs. In essence, VTE in automotive transforms your voice into the primary interface for controlling the vehicle, including everything from adjusting the climate controls, to navigating to destinations, or even managing entertainment options.  

Just Like Language Itself, Voice Technology Has Evolved Over Time 

The Evolution of VTE in cars isn’t entirely new, but it has come a long way from the rudimentary systems of the past. Early voice-activated systems often struggled with accuracy, limited vocabulary, and rigid command structures. However, recent advancements in AI and machine learning have dramatically improved these systems, enabling them to understand context, recognize natural speech patterns, and respond accurately even in noisy environments. Modern vehicles are now equipped with sophisticated voice assistants that can manage a wide range of functions. These systems are no longer just limited to basic commands; they can engage in complex interactions, understand conversational language, and even learn from user preferences over time. 

How Voice-to-Everything is Transforming the Driving Experience

The integration of VTE in vehicles offers several significant benefits, fundamentally changing how drivers and passengers interact with their cars.

To begin, VTE makes the driving experience more convenient and user-friendly. Instead of fumbling with buttons or touchscreens, drivers can simply speak their commands. This ease of use is particularly beneficial in complex, multitasking scenarios, such as driving in heavy traffic or during long trips. Modern VTE systems can learn from the driver’s habits and preferences, offering a personalized experience. For instance, the system can remember your preferred routes, favorite radio stations, or climate settings, automatically adjusting to your preferences as soon as you step into the car. 

Further, as vehicles become more connected, VTE plays a crucial role in integrating the car with other smart devices and services. Drivers can use voice commands to interact with their smartphones, smart homes, and other connected systems, creating a seamless experience that extends beyond the vehicle.

This hands-free approach is not only more convenient but also significantly enhances safety by reducing distractions.  By enabling drivers to control various functions without taking their hands off the wheel or eyes off the road, VTE greatly enhances driving safety.  Whether it’s making a phone call, changing a song, or setting up navigation, voice commands allow drivers to stay focused on the road. An additional benefit is increased productivity during long commutes, which significantly improves the driver experience. 

Finally, VTE is paving the way for the future of autonomous driving. As cars become more autonomous, voice commands will likely become the primary mode of interaction between the driver and the vehicle, allowing for smooth control of the car’s functions even when manual driving is no longer required. 

Let’s Drive Towards a Voice-Powered Future Together 

Voice-to-everything is rapidly becoming a cornerstone of the modern automotive experience. By making driving safer, more convenient, and more connected, this technology is set to revolutionize the way we interact with our vehicles. As it continues to evolve, VTE will play a crucial role in shaping the future of transportation, bringing us closer to a world where the sound of our voice is all that’s needed to command the road. Just to be clear, I am not yet ready to include my vehicle in my friend group, or as part of my fantasy team, but it’s clear that the voice-driven car is more than just a concept—it’s the future.  

As I’ve mentioned in a previous blog, Perficient is in the middle of conducting primary research on connected products. We also have a robust innovations lab that routinely helps OEMs with their customer experiences, data needs, and cloud infrastructure.  Please explore our automotive expertise and schedule a meeting, as we would love to discuss how we can help create a sustainable competitive advantage for you. 

Next-level personalization strategies will undoubtedly be a key topic at Salesforce’s largest event this year. Dreamforce is right around the corner taking place in San Francisco on September 17 to 19, and learning how to get the most out of your customer data with AI-powered tools is top of mind.

Whether you’re attending in person or virtually, the conference boasts numerous opportunities to hear how some of the world’s largest brands are using data and AI to create personalized journeys and promote better business outcomes.

One such brand is Nationwide. This leading financial services and insurance provider will be at Dreamforce this year sharing how it leverages Salesforce Marketing Cloud, an AI-powered digital marketing platform, to turn customer data into actionable insights that drive significant business outcomes.

Nationwide’s Personalization Story at Dreamforce

The session titled “Tailor Experiences with Einstein-Driven Personalization” will be on Thursday, September 19 at 12:45 P.M. PST in the Moscone South, LL, Content Pavilion Stage 2 and showcase a series of personalization stories from a variety of brands using Salesforce.

Nationwide’s AVP of marketing operations Zach Mason will share their journey to create seamless, personalized experiences for producers and customers using Marketing Cloud and how they partnered with Perficient to increase conversions, optimize customer journeys, and make impactful decisions faster.

So, make sure to add this session to your Dreamforce agenda to hear from real users. You’ll discover best practices to elevate your personalization efforts and maximize lifetime customer value using Salesforce.

Join Us at Dreamforce

Join us to explore the future of personalized marketing and more at Dreamforce. Our team will be onsite, ready to guide you through all the latest innovations.

Visit #PerficientDreamforce2024 to learn more.

Can’t Make it to Dreamforce?

Don’t worry! Schedule some time with us and let our experts fill you in. And stay tuned to our Salesforce blog for all our post-conference insights.

Generic travel brochures and one-size-fits-all itineraries are becoming less prevalent in today’s travel and tourism industry. Travelers crave truly unique experiences, and the industry is responding with a powerful tool: data. By harnessing the power of data and personalization, travel companies are unlocking a new era of customer engagement, satisfaction, and loyalty.

Bespoke Travel Experiences Powered by Data

Travel recommendations shouldn’t be generic suggestions that can be found by a cursory Google search, but rather curated experiences that anticipate your every desire. With data, that can be the case. By analyzing everything from past booking history to social media preferences, travel companies can build a rich profile for your travel dossier. This data goldmine allows them to personalize itineraries that cater to your specific interests. Whether it’s reservations to a hidden culinary gem for the adventurous gourmet who devoured Anthony Bourdain’s: Part’s Unknown or serene nature escapes for those who indicating that they want to get away and unplug. Data doesn’t just personalize experiences; it also fuels intelligent recommendations.

Unlocking Customer Loyalty Through Personalization

Personalization is no longer a perk, it’s the expectation. Travelers crave experiences that feel designed just for them, and data empowers travel companies to make the perfect menu just for them. Imagine receiving exclusive deals on flights to destinations you’ve been dreaming of, or automatic upgrades to experiences that resonate with your passions. By leveraging data and personalization, travel companies can build deeper connections with their customers thus fostering lifelong loyalty. This translates into repeat business and positive word-of-mouth recommendations as well as glowing five-star reviews.

Creating A Hands-Off Travel Experience with Customer Data

Data and personalization extend far beyond basic recommendations. Travel companies can leverage this powerful duo to elevate the entire travel journey. What if, when booking a flight, your preferred seat was automatically preselected based on past choices, or the hotel you book remembers your favorite room temperature and sleep number and adjusts accordingly upon arrival. Data can even personalize in-destination experiences. Instead of calling around for restaurants that can accommodate dietary restrictions, your recommendations will have already taken them into account. Rather than searching through various tour programs, they’ve already been curated so that they align with your historical interests.

On-the-spot Location Tailored Experiences

Location data adds another exciting dimension to personalization. You could be exploring a new city and receive real-time notifications about off-the-beaten-path cafes or historical landmarks that are right around the corner. Travel companies can use location data to send personalized offers for nearby attractions or cultural events, ensuring you make the most of every moment. Thanks to real-time suggestions and knowledge of your current location, you can be assured that you’ll be updated on impending weather conditions. This ensures a comfortable travel experience, providing a safe and cozy hideaway for you if there’s a need to duck in off the road and enjoy some shelter.

Personalization with Privacy in Mind

While travelers crave personalization, they also value privacy. The key lies in striking a balance. Transparency is crucial, allowing travelers to understand how their data is used along with giving them the power to control their privacy settings. Finally, travel companies must ensure data security and improve transparency about their policies to build trust with their customers.

AI Enables Travel Companies to Embrace New and Unknown Terrain

Data and personalization, especially enabled by artificial intelligence, will continue to evolve, and the travel landscape will transform with it. We’re entering a future where AI-powered travel companions will use data to anticipate your needs, suggest local experiences, and deftly navigate language barriers. Travel companies that embrace the power of data and personalization will be the ones who unlock the greatest opportunities, fostering strong customer relationships and defining the future of travel.

Forge the future of adventures and accommodations with our travel and hospitality expertise.

In this episode of “What If? So What?” Jim talks with Neelima Sharma, SVP of ecommerce, digital, and technology at Lowe’s about the challenges and successes of transforming the retail industry through digital innovation. Neelima highlights the importance of teamwork, trust, and a customer-centric approach in driving the company’s growth and also emphasizes the significance of personalization and building long-term relationships with customers. The conversation touches on topics such as the impact of COVID-19 on retail, the role of AI and human intuition in decision making, and the importance of accurate inventory management. The conversation provides valuable insights into the strategies and mindset required for successful digital transformation in the retail industry.

Listen now on your favorite podcast platform or visit our website.

Subscribe Where You Listen

Apple | Spotify | Amazon | Overcast

Meet our Guest

Neelima V. Sharma,  Senior Vice President, Digital Commerce and Technology

Neelima Sharma, SVP of digital commerce and technology at Lowe’s, oversees the company’s online business and customer- and vendor-facing technologies. She enhances omnichannel experiences, core merchandising, and competitive pricing strategies. With more than 25 years of experience, Neelima has driven significant tech advancements at Lowe’s, including migrating Lowes.com to Google Cloud, and previously led Staples’ $8 billion B2B ecommerce tech division. Her career also includes impactful roles at JP MorganChase and Deloitte.

Neelima holds a bachelor’s degree in computer science from the University of Delhi and a master’s from the University of Indore. Recognized as a leader in retail technology, she was named among the Top Women in Retail Tech by Retail Info Systems in 2023 and honored as a Top Woman in Hardware & Building Supply by HBS Dealer. Neelima is an adviser to tech startups, serves on the board of Charlotte Center City Partners, and co-chairs Lowe’s Asia Pacific business resource group.

Connect with Neelima

Meet the Host

Jim Hertzfeld is Area Vice President, Strategy for Perficient.

For over two decades, he has worked with clients to convert market insights into real-world digital products and customer experiences that actually grow their business. More than just a strategist, Jim is a pragmatic rebel known for challenging the conventional and turning grand visions into actionable steps. His candid demeanor, sprinkled with a dose of cynical optimism, shapes a narrative that challenges and inspires listeners.

Connect with Jim:

LinkedIn | Perficient

Sitecore Personalize uses a combination of experiences, decision models and offers to personalize content.  An experience defines a variant.  The variant is used to create the UI for the experience.  The decision model uses programmables and decision tables to select an offer.  The variant reads data from the offer to populate the UI.  By default, Sitecore Personalize expects the variant to have one user experience and for the decision model to return offers that all use the same offer template.  But what if you want to have different user experiences?  You could have a block of content with a title, image and cta.  You could have another block of content with a title, several icons each with their own block of text.  You could have yet another block of content with the image on the left and the title and description on the right.  Let’s look at some ways to do this in Sitecore Personalize.

Understanding the Pieces

Details of how the pieces of Sitecore Personalize fit together

• Experience: A block of personalized content and the required configurations.  Requires a variant.  Optionally includes page targeting, filtering and decision model.
• Web Experience: Useful for integration into websites
• Full Stack Experience: Useful for integration into websites and mobile apps
• Variant: The user interface for an experience.  Made up of html, css, javascript and api output.
• Html: The html markup for a variant
• Javascript: The javascript required to display a variant on the website
• Css: The css to style the variant
• Api Output: Dynamic data (usually from the decision model) that can be used to populate/modify the html and css
• Page Targeting: Defines which pages the experience will run on
• Filtering: Defines which users will see the experience
• Decision Model: A logical flow chart of business rules that determines the next best offer for the current user.  Made up of input data, decision table, programmables and offers.
• Canvas: The visual design tool for a decision model
• Input Data: Read data from the user, sessions and orders
• Programmables: Server-side javascript used to make decisions about the current user
• Decision Table: A matrix of data values mapped to offers
• Offers: The content that is used to populate the variant
• Offer Template: The field definitions (label and type) used to create offers

Multiple Layouts Option 1

Multiple web experiences, multiple decision models, multiple offer templates.

Visualization of option 1

You would create one experience, decision model and offer template per layout.  If your layouts share the same data fields, you can reuse the offer template.  If your layouts share the same logic, you can reuse the decision model.  In this case you will use page targeting and filtering to help control where and when the experiences will be displayed on your site.  Be aware that this option could have performance impacts as the number of experiences rise.  Sitecore Personalize will check every experience to see if it needs to run for the current page or current user.

Pros: Easy to manage each individual layout.

Cons: The page targeting, filtering, decision models, and offers can get cumbersome and complex to manage.  It is difficult to predict which experience will trigger on a given page for a given user.

Multiple Layouts Option 2

One web experience, one decision model, one offer template.

Visualization of option 2

You would create one web experience with multiple layouts defined in the html.  Use if statements to decide which layout is visible based on a variable in the api.  Include a number field in the offer template that indicates which layout to use.  Add fields to the offer template to support all the different layouts.  Don’t add fields to the offer template that you don’t need and try to reuse fields across layouts by using generic labels.  You might create a help document for content authors to know which fields are used by which layout.  Keep in mind that you cannot reorder fields on an offer template and you can only add new fields at the end of the list.  When you create an offer, all fields from the offer template must be filled out to save the offer (you can use NA on fields not required by the specific layout).

Multiple layouts inside one experience

Pros:  Only one experience to manage.  Only one decision table to manage.

Cons: Having multiple layouts in one experience can get a little confusing to preview.  Creating an offer requires knowledge of how the fields are used in each layout.

Multiple Layouts Option 3

One web experience, one decision model, multiple offer templates.

Visualization of option 3

This is similar to option 2 except that you would create one offer template per layout.  The fields can be labeled specifically how they will be used in a single layout.  This makes it easier for the content authors to create an offer based on the correct offer template for the target layout.  The decision model gets more complicated as you add more layouts.  You add a decision table for each layout/offer template.  You must assign your inputs to each decision table. The content authors are able to assign the correct offer to the correct layout.

One decision model with multiple decision tables

Be aware that this option could have performance impacts as the programmables are evaluated for each decision table on the canvas.

A programmable will run once for each decision table it is linked to

Pros: Only one experience to manage.  Individual offers are easier to manage.

Cons: The decision model is harder to manage as the number of layouts grows.  Each programmable is evaluated once per decision table.

Multiple Layouts Option 4 & 5

One full stack experience, one decision model, one offer template.
One full stack experience, one decision model, multiple offer templates.

These two options are the same as option 3 and 4 except they use a full stack experience instead of a web experience.  This means you will call the personalize api manually.  This can be done either server-side or client-side.  You will use the data returned from the api to populate the UI.

Pros: Complete control of UX.

Cons: You must call the personalize api manually and handle the response to populate the UI.  Requires developers to make the api calls as well as to make changes to the UX.

Conclusion

You can bend the rules of Sitecore Personalize to allow multiple layouts.  Consider the pros and cons of each solution and pick the option that is the easiest to manage while keeping the highest performance for your situation.

Shoutout to Megan Jensen for the fun visualization graphics!