It’s no secret that understanding exactly who your customers are, including how and when they interact with your brand, is the most critical aspect of your business. Being present during the crucial touchpoints of a customer experience is rapidly becoming table stakes to acquiring and engaging your target market.
Having the necessary data at your fingertips to know exactly when to be present during your customer’s experience can be a bit of a mystery.
What remains elusive to many organizations is how to actually go about connecting data from one customer interaction, as recorded by individual platforms, to one another. Whether B2B or B2C, capturing and integrating customer touchpoint data from content management systems, customer relationship management systems (CRM), enterprise resource planning systems (ERP), data management platforms, customer data platforms, marketing automation platforms, email, web analytics, mobile apps, loyalty programs, order management systems – the list goes on and on – can be downright daunting.
Customer data integration isn’t just critical now. According to a McKinsey study, integrating legacy systems with new marketing technology in a cost-effective way, while simultaneously developing a comprehensive and holistic view of the customer, will be one of the most challenging tasks companies will face.
Interestingly, we’re seeing a general reluctance to undertake this seemingly massive effort by much of the market, largely out of anxiety over the perceived scale, scope, and cost. Many organizations create an artificial delay in their digital transformation as a result of their struggle to understand how to begin this process.
In this first of a two-part series, we offer a bit of a different approach for those organizations without the significant budget and resources required to tackle a time-consuming and expensive holistic data integration. It flows right along with our Perficient Digital motto of “Think Big. Start Small. Act Fast.” When appropriate, instead of attempting to architect a completely new data model, we encourage some of our clients to consider a different approach. Some examples of this perspective include:
We highly encourage our clients to use a business-outcome lens to evaluate each use-case. Every potential integration point should be evaluated by specific business cases and the greatest likely business impact given the least relative effort.
For example, after understanding your top customer conversion point is via email, and realizing you need additional account information to segment and feed your email platform, integrating your CRM with your email/marketing automation platform might be the best place to start.
A mobile app that isn’t doing so well? Maybe move that integration to lower on the list.
Biting off key integration points, while keeping one eye on the total customer journey, is a great place to start.
Most importantly, this approach keeps the focus on how the customer’s interaction is actually impacting the business, followed by the actual data integration needed to make it happen, not the other way around.
Why connect data points without a clear plan for how to use said data? Whenever possible, there should be a single source of actionable data, and a customer data platform (CDP) or data management platform (DMP) are really good options. You don’t have to have everything connected right away, but getting a solid CDP stood up allows you to start funneling key data into a single source, which is solely designed to help you start making customer-impactful use of the data.
Once you have a data hub that is generating action, it’s also much easier to keep prioritizing future integration points.
This is becoming increasingly critical as a post-GDPR world continues to highlight the need for actual first-party, first-person data.
It might seem obvious that integrating data points is all about measuring the success of various customer touchpoints, however, we espouse measuring the measuring when it comes to data integration. Each new data integration provides a more holistic and comprehensive view of the customer, and each addition adds to the amount and type of usable data. Establishing baselines for current measurement, as well as future state objectives, is key prior to any kind of data integration to monitor multi-stage customer interaction and validate/adjust previous measurement models for each platform.
For example, a previous baseline/success for top-of-funnel media might be drastically different if a CDP is integrated with Adobe Marketing Cloud or another ad server. Suddenly geo-targeting and look-alike campaigns based on transactional data completely up-end your previous brand awareness and consideration metrics.
Make sure you have created measurement plans for each priority business case prior to data integration.
Data integration might seem like a massive effort, and for many organizations it can be. But there are alternatives to a complete overhaul. Prioritizing business cases, focusing on actionable data, and measuring everything as you go are valuable possibilities to get you started.
Be sure to come back for Part 2 in this series, where we focus more intently on using data integration to get a better handle on individual customers/customer IDs, prioritize channels based on engagement and lifetime value, and the importance of establishing a test and learn culture.