Once you access your data, how do you leverage that data to drive business forward? This episode will outline how to monetize your data and gain the necessary competitive edge.
In season 1 episode 7 of the Intelligent Data Podcast, host Arvind Murali and his guest Pawan Gupta, Perficient’s Principal of Commerce and Omnichannel Services, discuss the impact data has on ecommerce, supply chain, and order management as well as key considerations such as data privacy and compliance.
Listening Guide
- The difference between global commerce implementation and domestic deployments [2:09]
- Geo-political issues and product distribution [4:23]
- data sets used in implementations [5:47]
- Process Mining [9:35]
- Visibility into end-to-end supply chain [13:51]
- Monetizing your data [16:22]
- How to enable insights and data analytics across the supply chain [18:14]
- Using AI to automate the supply chain process [21:04]
- Pricing strategy [25:25]
- Startups and commerce [26:01]
- Enabling digital marketing [28:10]
- Customers and data privacy [33:20]
Get This Episode Where You Listen
And don’t forget to subscribe, rate and review!
Apple | Google | Spotify | Amazon | Stitcher | Pocket Casts
Connect with the Host and Guest
Arvind Murali, Perficient Principal and Chief Strategist
Pawan Gupta Perficient Principal
Learn More About Our Commerce Solutions
If you are interested in learning more about Perficient’s Commerce services capabilities or would like to contact us, click here.
]]>COVID-19 has undoubtedly affected financial services trends in 2020 and will continue to do so into 2021. Since the pandemic began, financial services organizations have been responding to the crisis with continuity plans to address everything from bankruptcies to people losing their jobs and ability to pay their bills on time. Now more than ever customers need to be supported with trust, transparency and data-based decision making.
In season 1 episode 1 of the Intelligent Data Podcast, host Arvind Murali and his guest Scott Albahary, Perficient’s Chief Strategist of Financial Services, discuss financial services trends, how data is influencing change in the industry, and what you need to think about as recovery from the pandemic begins.
Listening Guide
Financial Services Trends and Data
- Data and AI-based disruptions [2:14]
- Customer intelligence and the “Universal Banker” [4:03]
- Master data management and data governance adoption [6:10]
- How is big data being used? [11:12]
- How industry leaders are structuring their digital ecosystem [15:10]
- Robo-advisors and wealth management [17:40]
- Cybersecurity and data protection [22:59]
- Post-COVID advice for personalization and digital interactions [23:50]
Get This Episode Where You Listen
And don’t forget to subscribe, rate and review!
Apple | Google | Spotify | Amazon | Stitcher | Pocket Casts
Connect with the Host and Guest
Arvind Murali, Perficient Principal and Chief Strategist
Scott Albahary, Perficient Chief Strategist, Financial Services
Learn More About Our FinServ Solutions
If you are interested in learning more about Perficient’s financial services capabilities or would like to contact us, click here.
]]>Perficient announces the release of two business podcasts to kick off 2021. The new podcasts titled What If? So What? and Intelligent Data offer audiences expert insights on how digital technology can transform business and reshape customer experiences today.
Click on the logos below to listen to the first episodes.
What If? So What?
This podcast discovers what’s possible with digital and figures out how to make it real in your business. In Season 1, hosts Jim Hertzfeld and Kim Williams-Czopek, members of Perficient’s Digital Strategy team, interview experts and veterans of digital transformation to put the hype and big ideas into perspective by asking “What if?”, “So what?”, and – most importantly – “Now what?”
Season 1 includes episodes on:
- What if Everything Really is Digital?
- What if I Convinced the CEO to Embrace Design?
- What if You Could Do 5 Years of Digital Marketing in 6 Months?
- And much more!
Intelligent Data
This podcast investigates the value of data and technology to reshape your business. Join Arvind Murali, Data Chief Strategist and principal, as he explores data trends and topics that will help move businesses forward. In Season 1, hear Arvind and guests discuss the value data provides key industries, trends across enterprise technology, and the immense capabilities of data.
Season 1 includes episodes on:
- Data and customer experience trends in financial services
- Data and analytics, AI and data privacy in healthcare
- Business intelligence trends
- And much more!
Don’t Miss a Single Episode
New episodes will be released regularly, with new seasons planned for the future. Subscribe to both podcasts on your preferred platform – Apple, Spotify, Google, and Amazon Music – to listen along when new episodes are released.
Previously, I described the benefits of personalization and outlined the changes a company should make to successfully implement a personalization program. Today, I will outline what companies should be doing right now to prepare.
While many financial institutions are conceptually “on board” and heavily investing in various facets of customer intelligence, the “Netflix” of banking or insurance has yet to emerge. The main reason is that true end-to-end personalization is a challenge that requires developing new capabilities, including robust cross-channel offerings, cross-enterprise collaboration, a single view of the customer, and a new technology ecosystem.
- Start with a strategy
To be successful, you need to develop and implement a strategy that addresses not only technology and tools but also data management, employee skills, and organizational culture.
Creating a complete, accurate picture of any given customer is challenging. Practical personalization is an ongoing effort and continuous refinement. It involves both coordination and integration, along with the all-important human component. The right tools are also crucial for translating data into effective personalization.
- Define what you are offering
Think about how you can build a more direct relationship with your customers. What do you have of value to offer them? How can you use data to enable that value, and will customers find value in the information exchange required? How does this advance your customer relationships and improve how well you serve them?
- Get the message out
This is the time to consider which channels you will focus on to achieve your overall business goals and solve your business problems. Define the key stakeholders using the data, what changes might be required to accommodate how stakeholders interact with each other, and how that data needs to be collected, cleansed, corroborated, and connected.
These considerations will drive the appropriate technology, IT infrastructure, and skill enhancements that will facilitate your strategy.
- Implement a collaboration mindset
A collaborative culture is critical to driving a successful customer intelligence implementation. Look with a critical eye at your organization’s maturity with regard to collaboration. If your internal teams don’t share data with each other, then you have work to do. Improvement needs to be driven from the top. Decision-making executives need to be the driving force behind a company-wide collaborative culture.
Also, consider your organization’s internal skills and whether or not you need a partner to help you on this journey. Qualified data scientists and analysts are in short supply right now, and many organizations don’t have the right mix of internal skills to embark on a customer intelligence journey.
- Take your technology to the next level
Then, of course, you have to set up the necessary technology tools and systems to provide a data-driven overview of all customer interactions. Artificial intelligence and machine learning are critical components of customer intelligence, and appropriate care should be given to choosing the right mix of strategic products and platforms that will enable you to scale and address use cases enterprise-wide. Also, remember to look at your development throughput – make sure you have the capabilities to cope with the influx of development that will come your way.
- Determine best-practices for privacy
And you need to look at your data. Privacy by design should be at the core of any data-enablement strategy, with responsible governance as a foundation. Bring your IT, legal, and privacy teams together to create sustainable policies around data collection, processing, and management. Make sure your data is correct, clean, and formatted so that it can be matched with other attributes and signals.
Resolve to a single identity – an end-to-end system of identity is the key to successful personalization. And remember that your customers change all the time. Think about how you will maintain an accurate, up-to-date identity, taking into account changes in things like address, email, devices, name, or age.
Customer intelligence can appear a daunting task, with lots of moving parts to manage and groups to corral. A strategy and a plan will help sort out priorities and dependencies and ultimately reduces time to value.
Remember, customer intelligence transformation doesn’t have to happen all at once – it just has to happen.
To learn more about the state of personalization in financial services and how you can begin to leverage customer intelligence to champion personalization and win over customers, download our guide here.
]]>My previous blog analyzed customer intelligence and the benefits it provides. This blog will show the benefits of personalization and outline what’s required to implement a personalization program.
Financial services companies that have implemented personalization report positive results, including:
- 60% increase in revenue per customer
- 81% increase in customer retention
- 87% increase in customer engagement
- 75% increase in sales conversions
Those are significant figures! So much so that 60% of financial services companies are implementing a personalization strategy, 70% expect to increase personalization budgets, over 70% will invest in personalization technology, and 70% are getting a positive ROI.
These companies are experimenting aggressively with personalized search experiences, attribute-based contact routing, custom call scripting, and social media text analysis to know when and what to communicate to consumers, predict actions, optimize offers, and identify when they are ready to buy.
Challenges Require an Overhaul of Systems, Processes, and Leadership
Financial services companies report the following challenges to implementing personalization, which will require overhauling systems, processes, and leadership.
As mentioned earlier, it’s all about the data and how it is managed and analyzed. It’s complex and challenging, and most financial services companies know this. In fact, only 7% feel they offer advanced levels of personalization within their digital applications, 75% consider themselves “not adept” at using AI to provide next-best-action recommendations, and 51% consider data analytics a significant challenge.
The challenges financial services companies face in building a customer intelligence ecosystem are broad-based, encompassing technology, skills, strategy, investment, and education.
Data typically resides in multiple siloed environments. Because data is not meaningfully connected across these silos, it is less accessible and thus compromises insight into customers, partners, products, and sales channels.
Data silos often are reinforced by organizational silos. Different groups manage different data, and teams use different tools and, in many cases, rarely interact with one another. Fragmented databases make it difficult to organize information and create 360-degree customer profiles.
Legacy IT systems are often unable to measure responses or recommend next-best actions, while complex organizational structures and silos undermine coherence and focus.
The traditional business intelligence infrastructure that most financial services companies have built can no longer keep up with analytics needs. These solutions cannot handle the growth of new data types like images, audio, and video, and they do not give users insight into data origins or transformations applied. There are also an increasing number of business processes and analytics solutions that require real-time data access to support decision making.
Financial services companies want to develop more personalized marketing capabilities, but those not taking a customer intelligence approach and building an appropriate analytics ecosystem are missing a strategic opportunity for real differentiation. Because it is an ecosystem, it is comprised of many different solutions that need to be seamlessly integrated. This makes it imperative that all stakeholders are educated on the benefits, as well as the investments required, to build this ecosystem.
To learn more about the state of personalization in financial services and how you can begin to leverage customer intelligence to champion personalization and win over customers, download our guide here.
]]>My last blog dove into the customer data management challenges financial companies might encounter when starting the personalization journey. Today, I’ll address customer intelligence and the benefits it provides.
Personalization requires a solid data management and analysis foundation, and financial services companies are starting to leverage customer intelligence platforms to collect, analyze, and contextualize vast amounts of data. They realize that they cannot leave those vast amounts of data unexploited.
Turn data into insight into revenue
Customer intelligence allows you to better understand your customers’ preferences, motivations, patterns, and wants and needs by combining demographic data, transactions, second- and third-party data, channel activity, and sales and marketing history. It enables you to build deeper and more effective customer relationships. It is becoming a critical ingredient in making effective strategic decisions, and it’s the foundation of building future business intelligence capabilities.
From a technology perspective, customer intelligence collects data from multiple sources, combines structured and unstructured data, and leverages artificial intelligence, machine learning, business intelligence, data visualization, and predictive analytics. It helps you develop insights around things like propensity to buy, hyper-segmentation, personalization, next-best action, and forecasting. It’s these insights that lead to increased cross-sell and up-sell results, reduced customer churn, and improved customer experiences.
There are many great personalization use cases being thought about now in financial services. Firms need to mature their data orchestration capabilities from the ingestion and integration of data collected from multiple sources to delivery, including actions, channels, timing, and sequencing. They need to do this in order to achieve the personalization vision in many of these use cases.
To learn more about the state of personalization in financial services and how you can begin to leverage customer intelligence to champion personalization and win over customers, download our guide here.
]]>Previously, I discussed personalization’s future in financial services. Today, I will dive into the customer data management challenges financial companies might encounter when starting their personalization journey.
Data management in any financial services firm is complex. It needs to address integrating identifiers across available touchpoints and devices, customer preferences and interests, data sources that include first-, second-, and third-party data. Data management capabilities need to be capable of managing structured and unstructured data, preparing data for analytics, and, of course, protect personal data across the data supply chain.
To become more data-driven and personalize customer interactions, you need to address three key data management trends: volume, ubiquity, and user demands.
Volume
It’s a data-intensive business. Internally generated data like customer information, transactions, purchase histories, service inquiries, and claims information generate hundreds of millions of new data points on a daily basis. To add to this complexity, much of the data is unstructured.
Ubiquity
Data is also everywhere. The number of data sources, coupled with the rapid growth in storage capacity, computational power, and connectivity, has led to data being created and processed on an unprecedented scale. The nature of the internet, the growing number of interconnected devices, and the rapid pace of automation mean that data creation will continue to rise at a rapid rate.
Not only is there an explosion in the volume of data being generated, but the velocity with which it is transmitted and the variety of forms that data may take are also multiplying. Structured and unstructured data sets can take a number of different forms, encompassing traditional data, alternative data, and big data. Each data type varies in its maturity and usage, with less-mature types requiring a degree of specialized or even novel techniques for effective processing.
User demands
Today, almost every employee in financial services is a data analytics user who needs the speed and information equivalent to what they experience with personal consumer technology. Users are demanding self-service access to data and easy-to-use tools for decision support and trend identification. And, of course, they need to trust the accuracy and security of that data.
It is no surprise that the current data management landscape is complex – in our client work we see multiple data lakes, data warehouses, operational applications, mobile apps, online apps, call centers, and analytics solutions. Data can be, and usually is, located in hybrid environments, on premises, and the cloud, making it challenging to connect it all to drive meaningful insight into customers, products, and sales channels.
To learn more about the state of personalization in financial services and how you can begin to leverage customer intelligence to champion personalization and win over customers, download our guide here.
]]>“The future of banking is going to be very personalized — one to one. Banks need to become more relevant in the moment for a consumer who has a particular problem. The power of data and artificial intelligence can help us do that.” Lisa Frazier, Head of the Wells Fargo Innovation Group
Personalization is the future of financial services. It is becoming the key driver behind ongoing digital strategies, and the industry pundits echo that. Financial services companies say the top two challenges facing them are acquiring new customers and creating personalized experiences.
In an age where 64% of people would consider banking with a tech company, financial services companies are focused more and more on personalized customer interactions. Upwards of 75% of consumers are willing to share personal data as long as they feel that data is secured and is used in return for better advice, better deals, or a better experience across physical and digital channels.
These two things – customer willingness to share information, and the industry’s increasing need to be more relevant for a customer – are driving interest and investment in technologies like artificial intelligence, machine learning, and smart personalization.
Personalizing the customer experience is no easy task. Financial services companies face many challenges in building the foundational aspects of personalization tools, and these tools are essential to enabling the “authentic” personalization that many in the industry feel is necessary to remain relevant to a new generation of customers.
You will need cutting-edge technologies, such as artificial intelligence, to facilitate it. You will need to be able to orchestrate the integration of vast amounts of internal, external, and activity-based data. And, you will need various technologies to help manage it all, not to mention a culture that’s capable of exploiting what the data can offer.
Because, in the end, it’s all about the data.
To learn more about the state of personalization in financial services and how you can begin to leverage customer intelligence to champion personalization and win over customers, download our guide here.
]]>Recently, Perficient’s Scott Albahary, Chief Strategist of Financial Services, and Arvind Murali, Chief Strategist of Data Governance, presented a webinar that discussed what it takes for banks and insurance companies to acquire and retain customers.
The key, they say, is to create a 360-degree view of customers. Without a holistic view, a company can never truly personalize advice and offers, resulting in lost opportunities.
We live in the age of incredibly high consumer expectations and rightly so. Fortunately, with the right strategies and tools, financial services companies can work hard to meet them.
Watch the recording to hear Scott and Arvind discuss:
- Customer Data Management Challenges
- What Banks and Insurers Are Experiencing
- What Customer Intelligence Is
- Two Real-World Examples
- How To Achieve Success
In a race to gather, analyze, identify, and personalize messaging for customers to capture, acquire, and maintain customer relationships, organizations need a clear vision and strategy about their customers.
There are two parts of that strategy:
- Customer experience with an emphasis on design and empathy
- Customer intelligence with an emphasis on analytics and data
Next week, I, along with Perficient’s Financial Services Chief Strategist Scott Albahary, will be presenting a webinar “The Secret to Acquiring and Retaining Customers in Financial Services.” It will emphasize creating a six-tiered framework that enables the ability to capture, clean, and maintain customer information.
The customer intelligence framework can support the data-driven decision-making process for any line of business, as well as customer service, marketing, and product teams. The framework helps articulate business strategies in terms of customer intelligence architecture.
I look forward to you joining us.
]]>First of all, big kudos to first responders such as doctors, nurses, police, front-end-shop staff, and mental health workers for keeping your communities running during these troubling times. At the same time, my heart felt condolences for anyone who has lost a loved ones during this time.
One of the things that the COVID-19 pandemic has taught us is the value of data. Let me explain! Think about what you do first when you wake up. If you’re like me, you’ll open your trusted data source and check what the COVID-19 count is across different parts of the world. Just google “COVID statistics” and it returns 4,770,000,000 websites with its own dashboard automatically reflecting the state you are in and United States population (assuming you are in US).
Now here are the most trusted datasets that I personally use based on who they are and what they do:
- CDC COVID tracker
- Johns Hopkins Research Center
- Our world in Data (.gov)
- World Health Organization (WHO) Survey and Situation Report
- Every state website in the US
The Value of Data Through COVID-19
Why is this so important? What COVID has exposed us to is the value of data. If you were given 2 choices, a pretty website that doesn’t have trusted dataset for COVID and a normal dashboard that has the most trusted dataset for COVID, what would you choose? You would likely go with the website that has trusted data. In my opinion, every company should treat the value and importance of data similar to that. Gartner says that in order for companies to innovate “their way beyond the post-COVID-19 world, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to succeed in the face of unprecedented market shifts.”
Here are some key trends to note on the value of data and analytics post-COVID-19 world:
- Data marketplace – As we start exchanging data using online marketplaces internally or externally (paid or free), third-party datasets alongside first and second-party will become increasingly valuable to merge into your existing eco-system of datasets that you currently use. Gartner also stated that “by 2022, 35% of large organizations will be either sellers or buyers of data via formal online data marketplaces, up from 25% in 2020.”
- Dynamic metadata – This includes defining business metadata during runtime, technical metadata collected as statistics during loading and reading of datasets, and dynamic queries generated using metadata. As analytics and integration vendors are focusing on automation of runtime metadata, this concept will pick up steam and will become the norm of building a data platform very soon. This function will also play a key role in regulatory compliance and data security aspects of value of data.
- Customer intelligence – As organizations are striving to evaluate and segment customer acquisition and retention strategies, machine learning will play a critical role in gathering, analyzing, and providing recommendations regarding customers allowing organizations to focus on personalization strategies.
- Value of social media – Website traffic, social sentiments, and brand engagement are some examples of how data is being used to understand the brand interaction with the audience and their response to the constantly changing way of doing business and cater to their audience based on data and analytics. Platform companies such as Uber, Amazon, and Airbnb have mastered this art with their technology platform strategy bringing data and digital together in a single platform. This strategy will often prove valuable in customer loyalty and customer retention strategies as well.
- Reducing risks and improving safety – Many companies such as Salesforce, Domo, Oracle, and others have launched “get back to work” campaigns where the value proposition of these technologies are real-time streaming data in the hands of company CEOs who need to monitor their employee’s safety every day, every hour to run a business in these troubling times.
We have a lot of experience on this topic and will be happy to chat with you if you need help in digitizing your data sets and quantify value of it in that process.
]]>At the recent Adobe Symposium in Chicago, I had the chance to attend a presentation by <a href="https://twitter.com/drcedricalford?lang=en" target="_blank" title="Dr. Cedric D Alford, Adobe Microsoft partnership lead" rel="noopener noreferrer">Dr. Cedric D Alford</a>. Dr. Alford is leading the go to market strategy for the Adobe-Microsoft partnership.
To this point, I have to admit, I've struggled to see how these two industry giants benefit from a partnership, even though they do have somewhat complementary portfolios.
Why Adobe + Microsoft?
Luckily, Dr. Alford managed to distill it down to a simple value proposition: <em>Adobe and Microsoft are bringing together Marketing and Sales.</em>
Adobe is the leader in marketing platforms via the Adobe Creative and Marketing Clouds. Microsoft's Dynamics and PowerBI are industry leading tools for sales organizations. By bringing these two platforms together, Adobe and Microsoft are stronger than either are on their own.
Step 1: Clouds Converge
Now that we get the intent, how do we make that happen? Well, first, Adobe and Microsoft are literally bringing their clouds together. By running Adobe's Marketing Cloud on Microsoft Azure, Adobe and Microsoft's products can be more tightly integrated without the friction of external integrations.
Perficient is excited to support this collaboration! <a href="https://blogs.perficient.com/files/2017/07/AEM-Azure-Solution-Sheet.pdf" target="_blank" title="Learn how Perficient's Adobe and Azure expertise helps your cloud adoption" rel="noopener noreferrer">Download our paper</a> to understand how Perficient's expertise in both the Adobe Marketing Cloud and Microsoft Azure can help your organization adopt this cloud solution.
But Wait, There’s More!
Along with the cloud convergence, Adobe and Microsoft are bringing more to the table to bring together Marketing and Sales. The integration of Adobe Campaign + Dynamics and Adobe Analytics + Power BI both align to the vision of bringing Marketing and Sales together.
Insights 2.0: Digital Analytics Convergence
The <a href="https://blogs.perficient.com/2017/07/06/analytics-the-rise-of-customer-intelligence/" title="Digital Analytics + BI = Customer Intelligece">convergence of Digital Analytics and Business Intelligence</a> will be one of the biggest trends shaping analytics over the next couple years.
Customer Intelligence, or the convergence of Digital Analytics and Business Intelligence will remove the artificial barrier which exists in many organizations between how marketing and sales views customers.
In the last <a href="https://twitter.com/search?q=%23adobechat&src=typd" target="_blank" rel="noopener noreferrer">#AdobeChat</a> we discussed the future of Analytics and this convergence was a common thread.
Daniel! what up buddy. Convergence I think that is the one word answer on the future of analytics. #adobechat
— Ross Quintana (@Ross_Quintana) July 19, 2017
The integration of Adobe Analytics and PowerBI is a key part of the Adobe and Microsoft story. It enables any organization to build Customer Intelligence. In the next blog post, I'll be discussing how Perficient has been exploring this powerful integration!