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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The Hype Around Generative AI Continues

Many healthcare leaders are wondering if (and how) generative AI, the shiny new tool, could drive value in their organization.

Our recent discussions with Chief Medical Officers, Chief Information Officers, Chief Medical Information Officers, and a VP over Nursing point to this: AI can provide a huge amount of value when it comes to care delivery (e.g., point of care).

You may notice that I mentioned AI there and not, more specifically, generative AI.

Frankly, several of the most interesting care delivery use cases do involve generative AI, but they are not the only examples. And any truly innovative approach shouldn’t self-limit based on what’s absolutely hot in the market place. (Looking at you, generative AI.)

Many provider CIO’s and CDO’s tell us that the revenue cycle side of the equation is already supported by a number of helpful AI solutions. But, they stress, that doesn’t solve for some of the most vexing problems when it comes to clinician burnout. So in this post, I’ll focus on use cases for care delivery and how AI can help.

Care Delivery Use Cases: AI (and Generative AI, Too)

These use cases are just that – a list of possible uses for AI/ML and predictive analytics that drive some sort of value. AI can do a lot, but every use case assumes that the AI model or tool can be used with a system of engagement like an EMR.

SEE ALSO: Evolving Healthcare: Generative AI Strategy for Payers and Providers

I’ll break each of the use cases down by category:

Accelerate Imaging Decisions

We’ve helped our clients identify multiple imaging use cases, including the ability to:

• Flag radiology concerns: Use AI to review an image and perform a preliminary radiology modeling assessment. The focus is to help radiologists start with pre-identified concerns on a given x-ray, CAT scan, or MRI.
• Identify patients who need specialty support: This is similar to radiology in that an assessment occurs, but it would be for pulmonary nodules, liver transplant, kidney transplant, etc.

Streamline Referral Processes

AI could help to better manage the referral process. Of course, it would have to be paired with engagement technology and capabilities (which we also drive for our healthcare clients).

• Manage referrals: Receive a referral and apply an AI model to review referral data and determine the correct doctor or other component of the referral.  You can make this part of a referral management workflow which automates this step.
• Improve experiences: You can also automate the actual experience with the patient. communicate via the preferred channel, send emails or text with the referral info, and allow the patient to interact with an AI chatbot to schedule an appointment.

Support Operations With Deep Learning (DL) Insights

Delays in hospital throughput have negative impacts on financial and hospital optimization results. Insights derived from AI could help close the gap.

• Length of stay: Define potential length of stay in a variety of situations including pediatric, time study, overall patient population, etc.
• Readmission risk: Define the risk of readmission and identify the most likely cause of readmission; this AI model should identify the risk and then push the insight to an EMR.
• Left without being seen: Identify who will leave the ER before being seen.
• PT/OT/OT: Determine which patient should be treated with therapies; this becomes an aid to help the clinician diagnose and prescribe.

Support Patient Care Decision Making

Well-crafted models could support teams as they make decisions to support patient care.

• Testing decision-making: Define appropriate (and unnecessary) testing under certain conditions
• Evidence-based clinical decision-making: again, this AI model will help define clinical decisions that a clinician can then use in their decision making process
• Digital twins: digital twin data could be used to help define the overall best care for a patient

Auto Generate Medical Records

• Using ambient clinical intelligence, capture conversations between a patient and doctor or nurse and then generate an encounter in the EMR with any needed prescription or other information. Ideally, the doctor would review and approve the record and any actions coming from the visit. (This approach should be used for any patient interaction, whether it be a doctor, physician’s assistant, nurse practitioner, or nurse.)

Streamline Clinicians’ In-Baskets

Most clinicians feel buried in their in-basket and need help to quickly identify what needs quick action and what can be delayed or even automated. AI and generative AI could support in a number of ways:

• Better manage the in basket. Auto-categorize the message and make it easy for the important things to rise to the top. Auto-forward messages where appropriate. Auto-generate forms and filters to help in quick response to a given in basket message.

Support the OR

• Optimize the schedule: Based on a variety of factors
• Help with surgical correct counts: Help to automate or even review to ensure no foreign elements remain inside post surgery

Elevate Patient Charts

Every clinician reviews a chart before and after speaking with a patient. You can use AI models for variety of purposes:

• Auto-flag risk profile or patient risk scoring
• Auto compile quality data results (in many hospitals, this is still a fully manual process)
• Auto-chart review to auto-populate frequent screens
• Pull in SDOH predictors of delayed discharge

Support Quality Audits

• Auto compile data audits. Take the manual process, feed data to a model and compile a quality audit. This is technically a generative AI use case but one not top-of-mind.

Ease Digital Interactions, Using the Digital Front Door

What you’ll quickly notice is that this category differs from the others.  It deals more with digital interactions either before or after the point of care. That said, AI can still impact care delivery for things like correct identification of issues before a patient arrive.

• Use AI to schedule appointments. Remember that scheduling an appointment many times relies on a practitioner’s skills and knowledge before actual creating the appointment.
  • Is the physician seeing patients?
  • Is this a specialist who can only be scheduled under the right conditions?
  • Will the physician be in a location at the time the patient wants to schedule?
• Self-triage with a suggested appointment time and scheduling options
• Use AI in a symptom checker and pair it with a chat bot to push to an e-visit, if applicable

Better Outcomes for Patients and Clinicians

One thing you will notice is that care delivery use cases focus on two main needs: 1) clinician burnout – in essence, making a doctor or nurse’s life easier – and 2) better care for the patient – getting them to the right care more quickly. AI offers tremendous potential to create better outcomes for both patients and clinicians.

In my next post, I’ll focus on correct prioritization.

Expert Digital Health Services: Imagine, Create, Engineer, Run

Our healthcare and data experts can help you identify AI opportunities and build a pragmatic implementation plan that holistically considers data, technology, and people.

Drive Outcomes in Healthcare With AI

MicroStrategy World 2020 may be in our rearview, but now is a great time to start taking what we learned from the conference and figuring out how we can apply it throughout the rest of the year. For instance, how we can follow the trends that industry professionals are seeing in the field of analytics.

In his “Future Trends: Driving Analytics Adoption” session at MicroStrategy World, Ventana Research CEO and Chief Research Officer Mark Smith talked about analytics trends and driving adoption. Based on what we learned, here are 5 trends you can follow to maximize analytics adoption.

By 2020, 90% of business professionals and enterprise analytics say data and analytics are key to their organization’s digital transformation initiatives. – Research and Markets

Trends Driving Analytics Adoption

Using data and analytics to drive business decisions, better customer experiences, and overall digital transformation is a goal that most organizations share, but the path to adoption comes with challenges. While modern, cloud-based analytics and end-user self-service have helped increase adoption and the value of analytics, there are a few trends you can start following to bolster your success.

1. Embrace and Use Mobile Computing

Access to mobile analytics is greatly improving adoption and providing users with data immediately. Plus the widespread deployment of 5G will likely make access even faster and accelerate the mobile-first movement furthermore.

The voice and proximity on mobile provide personalized context to information. And Mobile computing with IoT and XR also provides augmented and virtual potential.

2. Embedded Analytics Everywhere

Users no longer have to switch between tools to find the data and insights they need. Instead, analytics can be integrated into a user’s day-to-day workflow. This seamless integration, enabled by Open platform, provides both internal and external users with immediate access to actionable insights inside of the applications and processes they’re already using in a context-aware manner.

3. Ensure Intelligence in Analytics

Embracing advanced, prescriptive, and predictive analytics tools is another driving force behind adoption. These tools can generate context and drive real insights. Specifically, using the Semantic model and graph to generate context and prescriptive and predictive analytics to drive real insights.

When generating context with advanced analytics, Semantic data modeling techniques are great because they can be used to define the meaning of data within the context of its interrelationships with other data. Basically, it can define how data relates to the real world. And this is key because users are more willing to see the value in analytics if insights are accompanied by context. Without context it’s difficult to take action and users are often left with more questions than answers.

Once users have context, predictive and prescriptive analytics can guide the next steps. It’s one thing to see the data, but knowing the story and suggested prescriptive action can make all the difference. Predictive analytics provides you with the raw material for making informed decisions, while prescriptive analytics provides you with data-backed decision options that you can weigh against one another.

Insights are worth a nickel, actions are worth a dollar – Mark Smith, CEO and Chief Research Officer, Ventana Research

4. Embrace NLP and Conversational Analytics

A general lack of data literacy among non-specialist users has made adopting analytics tools challenging, but improvements with natural language processing and conversational analytics are expanding the potential user pool. These tools remove the need to program queries into an analytics tool and make it easy to query databases. Conversational computing can process a large number of conversations (text and voice) at scale in the form of natural language processing and help bring immediate insights a lot more easily.

Furthermore, Gartner predicts that 50% of analytical queries will be generated via search, voice or NLP (or automatically generated) by 2020 and that NLP and conversational analytics will drive analytics and business intelligence adoption from 35% of employees up to over 50% by 2021.

5. Utilize Collaboration to Engage People

While a lack of data literacy is a challenge, so too is a lack of data democratization. Data and analytics play a key role in an organization’s success, but there’s a lot of missed opportunities if all staff members aren’t empowered to access, analyze, and act upon the information. Allowing staff companywide to inform on strategic decisions adds to the greater overall success of your business.

To empower your employees, Forbes recommends the following:

• Share the vision across the organization
• Emphasize “soft” skills
• Establish governance
• Focus on continual learning and improvement
• Develop a “data-first” approach

Takeaways for Analytics Adoption

Data and analytics tools have the power to help create or maintain a competitive edge in your industry, but the success of your data and analytics project depends on your people. Based on the five trends above, successful user adoption comes down to:

• Are your tools fast and convenient? Users are more likely to use analytics tools if they have easy access to them (mobile) and if they can find the data quickly (embedded analytics).
• Do your users have context and insight on how to take action against data? Users want quick, actionable insights that provide background information (advanced analytics) and suggested the next steps (predictive and prescriptive analytics).
• Do your analytics tools perform the heavy lifting? Users don’t want to spend an exorbitant amount of time programming queries or sorting through complex data. Allowing NLP and conversational analytics to do the hard work not only delivers insights more easily but increases the potential user pool.
• Do your users have visibility and knowledge? Democratizing data and providing all employees with visibility into data not only increases adoption, but also more informed business decisions. But granting access to data and analytics tools also requires proper training and education.

More Insights from MicroStrategy World 2020

Check out our other MicroStrategy World 2020 blog “Too Weak, Too Slow: MicroStrategy World 2020’s Big Theme.” We discuss how MicroStrategy is addressing the idea that existing enterprise applications were built for an outdated paradigm and are too weak and too slow to meet the demands of today’s users.

Many recent machine learning challenges winners are predictive model ensembles. We have seen this in the news. Data science challenges are hosted on many platforms. Techniques included decision trees, regression, and neural networks. And, winning ensembles used these in concert. But, let’s understand the pros and cons of an ensemble approach.

Pros of Model Ensembles

Crowd sourcing is better; diversity should be leveraged. We should choose the best model from a collection of choices. An ensemble can create lower variance and lower bias. Also, an ensemble creates a deeper understanding of the data. Underlying data patterns are hidden. Ensembles should be used for more accuracy.

Generally, ensembles have higher predictive accuracy. Test results improve with the size of the ensemble. That is why, ensembles are often challenge winners. Each technique has its own characteristics. For example, in data wrangling and tuning options. Tweaking makes models fit better.

With a bagging approach, each model should be tuned to overfit. Model independence is exploited because bagging is a variance reduction technique. Predictions can be softened for improved stability. These models are run in parallel and averaged. With boosting, models are used sequentially and wrong classifications from prior runs are given more weight. Boosting is a bias reduction technique. Stacking can be done with random forests. Stacking improves accuracy while keeping variance and bias low.

Cons of Model Ensembles

However, model ensembles are not always better. New observations can still confuse. That is, ensembles cannot help unknown differences between sample and population. Ensembles should be used carefully.

Is it understood? Ensembles can be more difficult to interpret. Sometimes, even the very best ideas cannot be sold to decision makers. Sometimes, the best ideas are not accepted by the final users.

Finally, ensembles cost more to create, train, and deploy. The ROI of an ensemble approach should be considered carefully. Generally, more complexity is not good in of itself. KISS. We have found that a full one-third of IS systems failure is due to complexity.

Take Away

Improved results are achieved by using a predictive model ensemble. But, what are we trying to do? Is it worth it?

I like seeing patterns across domains. Consider machine learning models and employees. Both models and people can be appraised.

What is an employee appraisal?

Employee “performance appraisal has three basic functions: (1) to provide adequate feedback to each person on his or her performance; (2) to serve as a basis for modifying or changing behavior toward more effective working habits; and (3) to provide data to managers with which they may judge future job assignments and compensation” (Levinson, 1976).

How is that like a machine learning evaluation?

All three apply to model performance. Model evaluation is one of the six tenets of the CRISP-DM framework. And the value of a model changes with time, hence, it is alive. See “concept drift” in Wikipedia or three reasons why models go out of sync (Pannu & Moore, 2017). Similarly, like employees, periodic checkups are good for models. The express goal is changing the model behavior and measuring usefulness for future assignment.

Humans are needed for employee appraisals, but maybe we can remove the personnel from the process of model evaluation. Consider automated machine learning (AutoML). “AutoML aims to automate the entire ML workflow” (Open Data Science, n.d.). Further, I suggest that machine learning can gain from general software development methods. For example, software methods that look to thrive on what used to be known as problems, for example, the Agile Manifesto or Antifragile Software (Kapalko, 2018).

The bottom line.

Appraisals are an important tool for your company. Disregarding appraisals is not the way because we know they have value. Consider a review of the appraisal process. A thoughtful appraisal can add value to the company and the people involved. Appraisals are a source of information.

Similarly, look for model evaluation. Consider checking the machine learning models that are aging. Further, look at the process of model review. As a result, company value can be increased. And improve the health for the people involved.

Let’s get appraising!

It’s no secret that data is a massive asset when it comes to making better business decisions. But gaining the valuable insights required to make those decisions requires quality data that you can trust. And to accomplish this you need a data strategy.

Without understanding your business objectives, identifying use cases, knowing how your users access data, and much more then you put yourself in the position of making decisions based on incomplete or incorrect insights.

Next month, leaders in the data industry will meet in New York City for the Strata Data Conference September 23-26 to share insights on how to implement a strong data strategy (as well as current hot topics like AI and machine learning, which need a strong data strategy foundation to build on).

Here are four sessions to attend to learn more about the elements of a quality data strategy.

Data Strategy Sessions at Strata

Foundations for successful data projects 
1:30pm-5:00pm, Sep 24 / 1E 10
The enterprise data management space has changed dramatically in recent years, and this has led to new challenges for organizations in creating successful data practices. Presenters, Ted Malaska and Jonathan Seidman, detail guidelines and best practices from planning to implementation based on years of experience working with companies to deliver successful data projects.

Running multidisciplinary big data workloads in the cloud 
9:00am-12:30pm, Sep 24 / 1E 14
Moving to the cloud poses challenges from re architecting to data context consistency across workloads that span multiple clusters. Presenters Jason Wang, Tony Wu, and Vinithra Varadharajan explore cloud architecture and its challenges, as well as using Cloudera Altus to build data warehousing and data engineering clusters and run workloads that share metadata between them using Cloudera SDX.

It’s not you; it’s your database: How to unlock the full potential of your operational data (sponsored by MemSQL) 
10:20am-10:25am, Sep 25 / 3E
Data is now the world’s most valuable resource, with winners and losers decided every day by how well we collect, analyze, and act on data. However, most companies struggle to unlock the full value of their data, using outdated, outmoded data infrastructure. Presenter Nikita Shamgunov examines how businesses use data, the new demands on data infrastructure, and what you should expect from your tools.

The ugly truth about making analytics actionable (sponsored by SAS) 
1:15pm-1:55pm, Sep 25 / 1A 01/02
Companies today are working to adopt data-driven mind-sets, strategies, and cultures. Yet the ugly truth is many still struggle to make analytics actionable. Presenter Diana Shaw outlines a simple, powerful, and automated solution to operationalize all types of analytics at scale. You’ll learn how to put analytics into action while providing model governance and data scalability to drive real results.

Visit Perficient’s Experts in NYC

If you’re attending the Strata Data Conference don’t forget to come visit us! Perficient is proud to be a Premier Exhibitor of the event and we’ll be at booth #1338 in the expo hall. Our experts will be onsite to strategize and showcase our expertise in complex data environments, AI, machine learning, and data strategy.

You can also connect with our team to set up a meeting, even if you’re not attending the conference. We look forward to seeing you.

Delivering seamless, consistent, and engaging experiences starts with a customer-centered digital strategy. This ongoing series explores the characteristics that make up a great digital strategy and how to deliver powerful brand moments that solidify customer loyalty and drive differentiation for your organization.

Ever-increasing and evolving customer expectations pose a challenge to businesses, regardless of their industry. Earlier in this series, we explored smart personalization as one approach to help brands provide more meaningful, relevant customer experiences.
Artificial intelligence (AI) is key to advancing personalization. It unlocks the potential for deeply understanding individuals, their preferences, and their journeys to create customer-focused experiences at scale. Organizations that want to maintain their competitive edge realize that embracing AI is a must.

31% of companies want to use AI to significantly improve the customer experience –Forrester

Among AI’s major benefits is the visibility it provides. Machine learning capabilities make it possible to cater to customers’ needs and exceed expectations. At the same time, AI’s predictive analytics can spot trends and opportunities for growth. Applying these insights ultimately makes it possible to create hyper-personalized messaging, deliver effective content, and precisely target customers.
To help you integrate AI into your digital strategy, I’ll discuss its role in delivering exceptional customer experiences, especially in customer support, and highlight examples of brands that are doing it right.

Get in the Game with AI

AI is here to stay, but we’re only scratching the surface of its full potential. An increasing number of experiences powered by AI, such as voice search, are reshaping how consumers behave and purchase items. This puts the impetus on leaders to continuously identify growth opportunities to meet the ever-evolving consumer expectations.
AI is also impacting the workplace. In fact, Gartner estimates that 70% of organizations will integrate AI by 2021 to assist with employees’ productivity. CMOs and others in the C-suite should recognize that embracing AI will make their brands relevant not only for customers but also for the teams they lead.
AI can alleviate many of the time-consuming, manual data analysis tasks marketers handle today. Its ability to sort through large amounts of data and provide analytics is a game changer.
However, this aspect of AI also “requires a workforce with a higher level of digital aptitude than what most organizations have today. Employing digital marketing talent that can adapt to the shift of AI as well as leverage and properly interpret insights from AI is essential.” Before fully launching AI into processes and operations, you must have the right organizational structure in place – with the right roles and people to fill them.

Making Sense of Embedded AI Capabilities

AI is embedded in nearly every platform now, whether it’s to drive personalization, support asset management, or provide sentiment analysis. For example, Adobe has built Sensei AI services into various solutions, including its Experience Cloud, Creative Cloud, and Document Cloud. Within Adobe Experience Manager (AEM), you can classify images. The built-in AI capabilities recognize and automatically tag images or allow you to look for specific images within your repository.
Considering this, you might need help understanding how to take advantage of these AI features on the platform. That’s one area – among many – where we help clients maximize AI’s potential.

Boosting Customer Experience with AI

A few of the solution areas that fall under the AI umbrella include machine learning, natural language processing (NLP), predictive analytics, cognitive computing, and signal services. These solutions help companies improve their understanding of customer intent, support customer service agents, and spot growth opportunities.

Machine Learning to Understand Intent

Many solutions you encounter today are trained to provide results but not answers. Chatbots and virtual agents have been common examples in the past few years. However, there’s a significant difference between the two. The “intelligence” behind chatbots has been scripted mainly by developers, creating scenarios to anticipate common questions asked by customers.
For example, if you’re shopping for a new car, you’ll likely start by visiting the auto manufacturer’s website. The chatbot on the site may prompt you with the usual, “How can I help you?” And you answer, “Which vehicles have all-wheel drive?” In this case, the chatbot responds with a list of results that show all the vehicles available with all-wheel drive. However, this puts the effort on you to refine and narrow your choices from this list.
If the website used a virtual agent instead, you could speak more conversationally and say, “I want to take this vehicle to the mountains.” Or, you might respond with a specific location in mind, such as, “I plan on driving to Pike’s Peak.”
The machine learning that powers the virtual agent understands the intent of your response. It learns and begins to understand the context of specific requests so that it presents you with options for all-wheel drive vehicles instead of you having to ask that question specifically.
You could try to manually script answers to every question a user could ask, but it would probably be impossible. Machine learning technology understands intent and how people ask questions. And over time, it gains that understanding so customers can engage virtual agents as if they’re talking to someone at a dealership. These agents understand what you mean and can make recommendations as you would have in real-life conversations.

Machine Learning to Improve Search Capabilities

We’ve also seen intelligence evolve with search engines. When you think of Google, you know you can ask the search engine a question, and you’ll be presented with real answers or even “people also ask” prompts to help you find the best answer.
Considering the intelligence behind search, you could use this to improve the effectiveness of your internal applications or intranet solutions. You could ask, “How much PTO do I have left?” The search application would connect to your workforce solutions, see the time you’ve used and the total time you can accrue for your role, and come back with an answer. Then, the solution could connect you to a PTO request process because it realizes you’re probably asking about your PTO balance with the thought of requesting time off.

Listening Tools to Assist Customer Service Agents

Some AI solutions assist customer service agents by listening to conversations with customers and recommending answers, policies, and so on.
Rather than having the agent navigate between three or four applications simultaneously, an AI solution embedded within the system can query multiple databases in real-time and present relevant information to the agent. This eliminates a step for the agent, allows them to focus on the customer, and reduces the call time needed to resolve or address the customer’s needs.

Identifying Trends Among Heaps of Data

Other AI use cases include combing through data to pinpoint trends and opportunities. It can analyze interactions occurring on your site, such as which products people view and items for which people are searching. Session replay tools provide funnel analysis and capture customer interaction from start to finish. You can review actions taken, where conversions happened, and whether or not the transactions were completed.
While there’s tremendous value in seeing how customers behave, consider the volume of recordings to review. When you have thousands – or even millions – of sessions, how is it feasible to manually comb through all that data? You can’t have one or two people sit there and watch them. Decibel Insights is an AI tool that can identify trends among hundreds of thousands of sessions. For example, customers may have problems on a specific page or viewing a specific product. AI excels because it aggregates data and points to potential issues.
Similarly, AI is great for sentiment analysis. With the ability to process datasets and customer reviews on a large scale, organizations can implement these tools and spot trends and growth opportunities.

Infusing AI into Your Digital Strategy

Even though AI is among the technologies that enable remarkable digital experiences, it can be embedded throughout your digital strategy. AI can include everything from content management and customer support to front-end experiences.
AI’s ability to anticipate customers’ needs and be more predictive is among the most significant outcomes. You can identify trends and behavioral patterns and use them to predict next steps.
Additionally, it can tackle personalization on a massive scale. As personalization has grown in recent years, brands have segmented audiences into five or six groups and then established and applied rules to create different experiences. But this “basic” personalization won’t stand the test of time. If your organization wants to deliver relevant, personalized experiences to everyone, artificial intelligence is the only way to do that. Cognitive solutions understand where someone is in their journey and the different factors and traits to deliver truly individualized experiences.

Sorting Fact from Fiction

Not every company is on the leading edge with artificial intelligence. Some still regard AI as “magical,” so some misconceptions exist. Contrary to what some may think, artificial intelligence can’t figure everything out.
In fact, you need to train them with machine learning solutions. Some platforms provide built-in AI capabilities that may help interpret and understand intent. However, the technology on its own can’t figure this out. You must train it to perform and operate in the way that best suits your needs. You must establish a foundation, add building blocks, and evolve the solution over time.

Start Small

We recommend starting with a small, narrow use case and expanding upon it. Once you see success with AI, you’ll identify other challenges to resolve and prove value over time.
You can implement AI in a small way because most of it is service-based. You don’t necessarily have to invest in implementing an entire platform. If you’re testing out improving a single product or solution using AI, you may not require the buy-in from leadership that more extensive efforts would need.
Once you’ve proven the value on a small scale, you’ll see bigger investments in the technology to expand and scale it across the customer experience.

Seeing Success with AI

Automotive

We recently helped one of our clients, a leading auto manufacturer, create a virtual agent to provide a differentiated – and improved – buying experience for customers. The objective was to help customers with their research and make the experience hassle-free.
The company’s leadership believes that most vehicles made today are built with quality in mind and features that better connect the driver and car. They want their brand to stand out from the competition – not based on their products – but with a unique buying experience. The brand is known for its “shopper assurance,” which offers transparency on the pricing of its vehicles.
To provide that level of transparency, the company wants to provide customers with tools to make this information as easy as possible. We’ve developed a tool that uses artificial intelligence to help customers find and purchase the right vehicle.
Most customers today are challenged by their limited knowledge and understanding of dealers’ terminology. The dealer’s view of products relates to model numbers, trim packages, and options within those packages.
As a customer, you don’t care about the terminology. Instead, you’re looking for specific features and functionality – leather seats, all-wheel drive, a sunroof, etc. Wouldn’t it be easier to research vehicles using an online tool that provides choices by looking at your preferred features instead of predefined packages?
We helped our client see this situation through the customers’ eyes and develop common questions asked when researching vehicles, such as the number of seats and storage capacity. For example, if a customer says, “I need a car that seats seven people,” the tool pulls the list of vehicles that meet that priority. Then, it asks follow-up questions around those options to narrow the choices.
As a result, we’ve helped this client in one phase of the customer journey to ease the car-buying experience and stay true to delivering “shopper assurance.”

Communications

While not one of our clients, Verizon provides a strong example of a brand that’s seeing success with AI. The virtual agent within its mobile app does a nice job of understanding intent by asking the right questions. Additionally, the agent excels at keeping customers in the experience to accomplish what they need to do, rather than redirecting them to its website or customer profile.
For example, you might ask, “How do I add a new line?” The agent responds, “I understand you want to add a new line. You can do this in your account. Do you want to do that?” This allows you to continue with that experience and guides you through the end of the process. However, you can also ask questions along the way at any time.
In this case, AI serves as a copilot to walk you through processes like a human customer service agent could, but it’s all powered by technology – pretty incredible.

Final Takeaway for CMOs and Marketing Leaders

Advancing technologies, like AI, make it possible to be successful marketers and meet customers where they are. You have to think about the best ways to leverage AI for the experience you’re trying to create. It should be considered for every solution, but there are many untapped applications for AI because not all the use cases are defined.
As marketing leaders, you must understand your customers, their expectations, and their journey to find opportunities to incorporate AI and take advantage of its potential for creating better solutions and experiences.

Creating stand-out digital customer experiences that attract, engage, and retain customers is a tall order. Perhaps you’ve already done some foundational work and need help with the next step. When working with clients, we help ensure you know your customers and understand their journeys. You will have what it takes to deliver experiences that surprise and delight your customers through design-thinking tools, industry research, and pragmatic ideation to execute from end to end. Contact us.

Change happens in the business world. The telecommunications industry is a principal example of the fluid, dynamic and rapidly evolving business environment. The dividers between telecommunications, technology and entertainment are becoming less-and-less prevalent; thus creating an entirely new telecommunications industry.

Blockchain Technologies

Blockchain in the telecommunications industry isn’t a crazy idea to fathom at all. In fact, pairing with blockchain technologies assists leading telecommunication companies to become more efficient, and reduce costs. But the biggest benefit of blockhain is the high level of security it provides.

Currently, the telecommunication industry faces incredible security concerns. Blockchain technologies address these concerns by breaking down the chain of work. Thus, maximizing company efficiency with streamlined internal processes and stronger encryption protocols. With one single view of work, a telecommunications company can immediately notice breakdowns, or security concerns; thus having the instantaneous ability to correct the issue.

Blockchain in the global telecom market is expected to grow to reach revenue of $1.37 billion by 2024. It’s clear these technologies will have a big impact. Telecommunication companies are already implementing blockchain technologies to focus on customer needs, and increasing overall profitability through optimal workflow opportunities.

Artificial Intelligence (AI)

Imagine optimizing artificial intelligences (AI) ability to engage in proactive learning while evaluating large quantities of data. In a telecommunications setting, applying these learnings open up a major assortment of practical use cases within both company operations and management, as well as supporting a range of revenue-increasing applications.

Currently, AI in telecommunication focuses primarily on network management. Still, AI can be more advantageous if the networks are upgraded and lead to the implementation of increased cloud-based services and overall network virtualization.

Another benefit of AI in the telecommunications industry comes with the implementation of virtual assistants. These assistants provide a cognitive opening conversation with customers to get quick information and provide simple solutions to their queries. Virtual support for customers provides an efficient usage of time, so much to the extent that these one-on-one conversations efficiently projects business expenses to be cut by $8 billion over the next five years.

Implementing artificial intelligence into the daily workflow regarding labor-intensive and time-consuming processes optimizes efficiency. Daily tasks done instantaneously via virtual methods allow the staff to be open to new and higher value-based work. Which then engages the workforce more, and enhances company moral and efficiency.

Predictive Analytics

AI predictive analytics are aiding telecom companies to provide superior services by applying data, refined algorithms and machine learning techniques to calculate future outcomes based on historical data. Calculating the future is key. With higher quality and a longer set of historical data, the better the predictability for the future.

Implementing this, telecommunication companies can work with data-driven insights to evaluate the state of equipment, predict future flaws based on patterns, and proactively service communications hardware, such as cell towers, power lines, data center servers, and even boxes in customers’ homes.

5G Connectivity

 5G brings three new aspects to the table: greater speed (to move more data), lower latency (to be more responsive), and the ability to connect a lot more devices at once (for sensors and smart devices).

This connectivity is about much more than smartphones. Sensors, thermostats, cars, robots, and other new technology will all connect to 5G one day. The 5G network is anticipated to assist the fourth industrial revolution, the growth of artificial intelligence, expansion of robots for use across telecommunication sectors and other industries. Facilitating machine-to-machine communications is also a possibility.

It will be exciting to see what kind of benefits 5G technology can bring to the table. Once the broadband technology is final, connectivity will be within milliseconds. This is a ground-breaking step, in a technologically advanced world.

Implementing these technologies and remaining proactive instead of reactive, will give telecommunication companies the proper tools to keep customers happier with a quality experience, thus building customer loyalty.

Most businesses today wouldn’t know what to do without their customer relationship management (CRM) system. Increasingly, most wouldn’t know what to do without their cloud-based CRM system specifically, either. It is this reliance that shows how important modern CRM systems are to customer care.

The way in which businesses have used CRM systems have changed, but they remain vitally important. This is shown by 92.2% of small businesses, 98.2% of medium-sized businesses, and 99.7% of enterprises keeping their customer data in CRM systems.

Today’s CRM systems carry out more tasks than ever. They don’t just allow businesses to store information about their customers. At their base, they allow businesses to target, acquire, understand, and collaborate with customers.

On top of that, modern CRM systems are used for contact management, activity management, lead management, sales management, relationship management, sales processes, customer intelligence, project management, forecasting, revenue management, partner management, team selling, and vendor management, among other things.

Modern CRM strategies match these capabilities. These strategies allow businesses to utilize differentiated customer experiences and truly utilize their data. It’s because of this that cloud-based CRM systems are the future – and why many businesses have already made the jump.

Rise of cloud-based CRMs

The rise of cloud-based CRM systems has matched the general acceptance of cloud. In fact, cloud-based CRM spending is already higher than non-cloud-based CRM spending. Salesforce is a driving force for the growth in cloud options, and businesses are embracing it. More enterprises have been attempting to integrate CRM apps onto the cloud in recent years.

Vendors have focused on producing these apps that solve very specific issues, catering to businesses that purchase CRM systems to address specific needs. This is a shift from old CRM models, which were an all-encompassing way to aid sales, marketing, and customer service.

Businesses are looking at AI and predictive analytics in particular as ways to use their CRMs. This allows businesses to cater experiences to their customers and understand what these customers are looking for. This is especially the case as customers continue to look to self-service – an area that the cloud thrives in.

Benefits of cloud-based CRMs

The specialization of CRM apps matches a greater trend in cloud and has brought development in online customer care. As well as AI and predictive analytics developments, APIs also enable automation. This is predicted to go even further, with cloud-based CRMs potentially evolving to include actionable customer intelligence that is gleaned from interaction data.

Other specialized services for cloud-based CRMs include subscription management, increased intelligence throughout systems, project management services, and operations. Vendors are essentially looking to build smarter CRM systems through the cloud.

As well as this, cloud-based CRMs also enjoy all of the benefits that the cloud as a whole brings. Crucially, this includes the ability to access information from anywhere. Regardless of location – or department – employees are able to serve customers. What customers want is fast and accurate care, and the cloud enables that.

Other crucial cloud services include data backup, automatic software updates, and scalability. When dealing with such vast amounts of data, all of these things save time, worry, and money. All of these savings can then be passed onto customers, improving their experience.

What cloud-based CRMs do is enable a better human connection for customer care. That may seem counterintuitive, but the intelligence of the cloud has improved CRM systems in ways that help customers. Businesses are flocking to cloud-based CRMs as a result.

Investing in resources that digitally transform customer care will provide a competitive advantage for your organization. Learn more in our guide Top Technology Trends for Smarter, Strategic Customer Care.

Thrilling our clients with innovation and impact – it’s not just rhetoric. This belief is instrumental for our clients’ success. In 2018, we announced the first class of Chief Strategists, who provide vision and leadership to help our clients remain competitive. Get to know each of our strategists as they share their unique insights on their areas of expertise.

Artificial intelligence (AI) is among the fastest growing technologies today, both in capability and implementation. It is an exciting, fast-paced area, with advancements occurring by the minute and adoption rates steadily increasing. AI is essential to any company’s future strategy.

Christine Livingston, AI Chief Strategist, leads our artificial intelligence practice with 10 years of advanced technology experience. We recently spoke to Christine and learned more about her role as a chief strategist, her perspective on the future of artificial intelligence, and her life beyond the role of chief strategist.

What does your role as a Chief Strategist entail?

My role at Perficient is to lead our artificial intelligence team. Our team focuses on creating solutions leveraging AI capabilities, optimizing and automating processes, and helping customers develop and implement strategies to adopt artificial intelligence. I help many clients develop AI roadmaps, including the human elements of the strategy, such as governance and human capital requirements.

Best practices for AI are still in the early stages of development. I think the role of Chief Strategist is especially important because AI is still in an early adoption cycle; having the personal experience of been-there-done-that and proven successful deployments in this space is quite valuable to our customers.

Strategically Speaking

What do you see happening with artificial intelligence in the future?

Artificial intelligence is a hot topic. It’s everywhere, permeating our culture outside of work. Watson even appeared in a Super Bowl ad! With that context, there’s a general perception that AI is widely deployed today, but it’s actually not. Gartner published a survey of CIOs in 2018 that showed only four percent had actually invested or deployed artificial intelligence to date and 46 percent were planning to deploy.

I think [AI] will continue to develop rapidly, and we will see wider adoption both in corporate and individual settings. AI’s greatest strength is also its greatest challenge. The technology is evolving so quickly and prescriptive progress is hard to predict. Today’s strategy will result in a roadmap for 6 to 12 months, and the technology will be vastly advanced by then. One of the things we’ve learned, and we typically recommend to our clients, is that they leave room for iteration and be willing to adapt and evolve with the technology.

It will also be interesting to see what happens from a build versus buy perspective. Meaning, there are many platforms doing some really interesting things right now. While at the same time, a lot of companies are also trying to influence the same concepts and build their own solutions. [Companies] want to own their data and own their models. It will be interesting to see what the open source, “build-your-own” community does in relation to some of the larger players in the space.

Why does strategy matter for deploying artificial intelligence solutions?

AI is not widely adopted enough yet for people to say, “Here’s the typical path or roadmap.” It’s really important [for companies] to understand the training process and the value they seek to drive, rather than just attempting to implement use cases prior to that fundamental understanding. There’s typically a natural progression when you look at deploying these solutions so you can start [achieving] economies of scale.

As an example, clients have come to us and said, “We have four or five different proofs of concept and technologies running on different platforms (because everyone went out and they did their own proof of concept.) Now we need to actually do something with it.” Realizing that they need to put it in production and industrialize it, they ask, “What do we do with all of these platforms?”

The challenge is everyone’s deployment of AI will be different because it’s so specific to your business. We are helping clients look at their particular pain points and goals, where artificial intelligence is driving value, and helping them come up with a strategic roadmap for implementation. [Strategy] is going to be critically important over the next year or two, as we continue to see early adopters [deploy] their first solutions.

Think Like a Chief Strategist

How does your team help clients on their digital transformation journey?

Among the major concepts regarding digital transformation is this notion of omnichannel, 24/7 support, and continual connectivity. We’re seeing many companies starting to deploy artificial intelligence within customer service functions or on the customer experience side to drive true omnichannel, consistent experiences across all those entry points. If you train and deploy a central AI platform, then you can expose it across different channels to fuel that experience and, ultimately, you’re using the same intelligence and decision making process on the back end to drive objectivity and consistency. We’ve done a lot of work in the virtual agent space to reduce costs in call centers and create an omnichannel customer experience.

Another way we’ve helped our clients digitally transform is with text analytics, which is essentially deriving meaningful information out of unstructured text. For example, in a manufacturing environment, we analyzed customer feedback and survey data to identify product level defects and influence its engineering change lifecycle based on that information.

From the analytics perspective, digital transformation is about optimization. Companies should ask, “What’s the best decision I can make given all the information I have?

In healthcare, we’ve worked with clients on challenges such as patient population identification, optimizing patient outcomes, and readmission indication. Unstructured data, which is effectively invisible to traditional analytics systems, contains a wealth of information.  For example, in the readmission indication use case, it’s important not only to understand if [ patients] will likely be readmitted but also to identify and address the underlying factors to ultimately improve their outcomes. Healthcare providers can now implement care plans based on what they know about the patient holistically to prevent future readmissions.

Automation is another explosive area where we are working in concert with technologies such as robotic process automation (RPA) to minimize required human capital and elevate the tasks on which employees are working.

How is your team helping clients with their AI strategy?

Creating a strategic approach [for AI] can minimize or eliminate some of the previously mentioned pain points. We’re seeing successful implementations with companies that take the time to identify and prioritize the right go-first use case. These use cases are big enough to drive value, but not so small that you can’t train for it and realize value in a reasonable timeframe. Clients that are willing to take more of a strategic, slightly slower approach have differentiated themselves in terms of AI success. It may take a bit more time, but in the long run, they end up with a better outcome.

In addition to strategy, we help on the platform side with guiding the selection process for the best AI platform for your company. If our team is engaged early on, we can also help clients avoid losing time, money, and effort to optimize their training cycles. This is really important because companies typically need to involve highly-paid subject matter experts (SMEs) in the training of these platforms.

Healthcare SMEs include physicians and nurses. In the legal space, you need your lawyers. For financial services, your financial advisors are the SMEs. All of these examples are typically very educated, highly paid, and high-demand roles. If we need leverage their time to train technology, then we want to use it wisely.

What else is important for organizations to understand for achieving success with AI?

Realize that the technology is never going to be 100 percent accurate. Achieving accuracy or confidence in the 90 percent range is equal to or above the output you’re getting from people. I’ve heard clients say they must be at 100 percent or they won’t deploy AI, but it is not realistic.

People are not perfect, nor 100 percent accurate. I think that’s a misconception [with AI] that you have to address. You have to understand the cost-benefit analysis of continuing to train the platform for accuracy gains. You need data to be “accessible,” clean, and accurate before you’re ready to deploy a full AI solution.

Beyond the World of Strategy

Tell us about yourself and your interests when you’re not wearing the Chief Strategist hat.

My family is a huge part of my life outside of work. My three-year-old started playing soccer for the first time. I played soccer all the way through college, so that’s something I’ve always enjoyed and loved. I’m excited to be joining the ranks of the soccer moms.

I also love to travel, which we haven’t done as much since having small children, but we try to take an international trip annually. I even flew my oldest, who was just one-year-old at the time, to Australia, just the two of us, to meet my husband!

Follow along on this series to learn more about each of our Chief Strategists. And, take a look at recent blog posts they’ve written on trending topics for their industries.

Consumers are digitally savvy, well informed, and more demanding than ever. Not investing in resources that digitally transform customer care will become a competitive disadvantage.

This series explores six technology trends for delivering smarter and more strategic customer care.

Trend #1: Analytics Help You Anticipate Customer Needs

Data and analytics go hand in hand to improving customers’ outcomes, streamlining operations, and optimizing marketing campaigns, among other things.

Predictive analytics makes it possible to apply past solutions to upcoming problems in new ways by leveraging AI and machine learning to interpret your valuable data.

Take a call center for example. A variety of of predictive analytics can help improve key performance indicators (KPIs) such as average wait times, customer satisfaction, and call completion rates. These analytics tools include:

• Speech Analytics allows you to improve communication by analyzing recorded calls.
• Text Analytics lets you review and monitor messages sent both to and from customers via social media or any other text messaging platform.
• Self-Service Analytics enables users to input their own information (e.g. phone number, age, etc.) into your database.
• Cross-Channel Analytics looks not only at which channels are driving customers to you but from a customer care perspective, looks to allow data to be accessed across channels.
• Augmented Analytics automates insights using machine learning and natural-language generation, which according to Gartner, marks the next wave of disruption in the data and analytics market.

It’s one thing to collect the data, but it’s another to apply the feedback. With the insight you gain from predictive analytics, you will be able to make informed, strategic decisions for your company and customers.

Don’t let customer feedback go into a black hole.

Learn more in our guide Top Technology Trends for Smarter, Strategic Customer Care and take a look back at previously posted trends.

The adoption of machine learning (ML) is increasing at near-breakneck speed. As organizations seek innovative ideas on how to improve the business, Oracle Analytics Cloud with ML capabilities is leading the charge. With built-in drag-and-drop functions into visualizations and autonomous prediction execution, Oracle Analytics puts the power of machine learning in your hands.

Join us to learn how Oracle Analytics can connect various data sources, allow you to apply ML without being statistically savvy, and easily build your story in presentation format.

Discussion will include:

• In-depth look at Oracle Analytics Cloud
• How to connect different data sources like SaaS applications, data lakes, external data sources and more
• Custom-trained ML models demonstration
• Real-world business use case from end to end

We’d love to have you attend our live event, but if you’re unable to make it, all registrants will receive links to the presentation materials and a recording of the on-demand webinar post-event.