Customer Data & Analytics Blog

Why AI is Crucial to Drive Omnichannel Customer Intelligence

Jeff Whitney | 2 minute read

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E-Commerce and mobile, in particular, have changed the marketing game. As we all know, customers have an unprecedented number of channels through which to evaluate, purchase, and otherwise interact with a company. In fact, a recent Practical Analytics report shows that many customers use an average of five different channels to contact companies, switching seamlessly from the web to the phone, to social media, to a physical store, to chat depending upon whichever is most convenient.

This creates a wonderful opportunity and incredible challenge for Omni-channel marketers. Treat customers appropriately and get rewarded. But watch out if you don’t. Per a recent Right Now survey, 86% of consumers say they are willing to pay more for a better customer experience. Alarmingly, 89% of customers left to go do business with a competitor after a poor experience.

So how do you give your customers great experiences? It all comes down to two things:

  1. Quality Data– Only with up-to-date, quality data can you hope to have an accurate view of your customer. To achieve this, you must gather all the relevant customer data that you can; from all possible channels, internal and external, and both structured (e.g. CRMs, POS, e-commerce) and unstructured (e.g. email, social media) as fast as you can and constantly keep it fresh.
  2. Quality Analytics– Driven from the data, use deep analytics to create the insights that drive all your customer interactions.

It all seems simple enough, right? But it’s not:

On the data side, marketing typically ends up with a limited, aged, and rigid view of customers.

  1. Limited: Gartner reports that 90% of most companies’ data goes completely unused, left to rot in data silos. Our experience is perhaps a bit more generous, as we see marketing typically getting 10-15% of the needed data.
  2. Aged: It takes even the best big data project weeks, and usually several months, to wrangle the datato drive analytics, even when the best, hard-to-source data engineers and scientists are available, so data isn’t nearly as fresh as marketers want or need. 
  3. RigidRequests for a new data source or a different view causes the “data” teamto take several steps back in their process and can take many more weeks to address. This turns “agile” marketing teams into “wait for it” teams.

On the analytics side, it’s an equally dismal view as, without a solid foundation, analytic systems can only construct surface-level insights from the data they access.   

  1. Propensity scores suffer: Propensity-based upon limited variables, incomplete data, and limited views are too simplistic when customer behaviors are so complex.
  2. Rules-based approaches don’t work either: Customers are just too complex to try and capture every user in a rule-based series and will often put the wrong message in front of a large percentage of users.
  3. Forget “Gut Feel”:Any companies that eschew the data and derive their marketing strategy from “gut feelings” are more likely to fail than ever. The customer, their journey, and their overall behavior are just too complex and uncontrollable.

AI to the Rescue

It may seem counter-intuitive, but Artificial intelligence can save the day to provide both the quality, fresh data and the personalized insights to drive extraordinary customer intelligence. Only with AI can you process the vast amounts of data, in a timely and continuous fashion, to produce quality data to be analyzed. And only with AI can the 100s of thousands or millions of variables associated with customers be processed. and personalized intelligence generated, for each customer. Fortunately, this isn’t a vision. It’s available today.  

Look to future posts where we’ll discuss in-depth how AI addresses both the data quality and the analytics challenges.

Of course, we’re always interested in your insights and experiences. And to learn how your company is addressing these challenges.

Topics: Personalization