API Management / Culture / Data / Contributed

Dynamic Data Capture Sets the Stage for Extreme Personalization

22 Jul 2021 3:00am, by

I recently traveled on a ferry between Larkspur and San Francisco. My provider could connect the dots: I am a sports fan, sitting on a ferry near SF with time on my hands. So, my mobile lit up, offering up some local sports information. This is extreme personalization (EP) in action. They didn’t sell more minutes to me, but they still gave me value and received a little more loyalty in return.

EP requires engagement for context, content and behavioral data. Combined with the advancement of AI-powered marketing automation platforms, extreme personalization isn’t just possible, it’s now emerging as a competitive advantage for modern brands and companies alike.

Today, customers expect more than personal engagement, they also demand engagement at the right time, on the right device, with the right message, hence the importance of extreme personalization.

Why EP Matters

Jeff Morris
Jeff Morris brings three decades of product marketing experience to Couchbase. He’s been head of marketing at data integration startup, Datacoral for the last six months, and prior to that, he was head of product marketing at Neo4j, helping graph databases break out from the NoSQL market. Jeff’s product management background was formed at open source pioneer, Sendmail and sales automation vendor Borealis, and he learned the fundamentals of software engineering and marketing at Forte Software and IDE. He studied Electrical Engineering and History at Syracuse University.

The pandemic and its push for rapid changes in consumer behavior changed EP into a critical feature every company must consider. EP means differentiation when transactional services aren’t enough to attract business. As exemplified above, telecommunications companies can’t compete on price alone and look to EP to deliver value-added services that reduce churn.

A more current example is food. According to the Food Sourcing in America 2020 report (Hartman Group), 27% of US shoppers say they shop online for groceries more now than before COVID-19. Another report from Hartman, from 2019, reveals that food personalization is a crucial driver among today’s consumers.

Shoppers expect personalization to make their choices easier. It’s not necessarily about knowing what to market to a shopper, but rather understanding their preferences and making those preferences count when a shopper needs them (and through the suitable device or channel of preference). Just as your spouse tends to know where you put your keys, EP speaks to knowing the right things about a customer to enable and support them at the right times.

Let’s say I go to the store and, for the first time, pick up strawberries. The shop’s system should note this preference, adding it securely and quickly to a dynamic user profile. Then, to grossly simplify things, algorithms do the rest.

I simplify because I want to focus on my point for this column: EP is not feasible without using a flexible and dynamic non-relational database platform.

EP Needs Dynamic Data Capture

Companies get caught up in the hype of EP, not realizing that you have to get the fundamentals right, or else the data leads the developers. How you capture data entries is one of the earliest and most significant barriers against successful EP.

Many EP workflows fail at the start. Most systems can’t create such an entry for my newfound strawberry craving on the fly. It would typically take considerable design and programming time to introduce a new category to record “strawberries.”

The second challenge is that many transactional systems cannot introduce such dynamic data entry features. Their owners are very reluctant to make any substantial changes to those systems in fear of breaking them. Overcoming the legacy barrier is essential and one of the most cited reasons why personalization projects fail. Gartner reports that nearly one-third of companies have limited or no capability to support personalization efforts.

NoSQL solutions address the first barrier. You can use the flexibility of a JSON document underpinning the application to write a new attribute immediately. You just discovered that I like strawberries because I put strawberries in my cart. Capturing that second attribute — without explicitly planning for it or stopping the application — is crucial to functional EP.

Fear of disturbing legacy systems shouldn’t prevent EP. Breakthrough migratory processes being developed at NoSQL pioneers such as my company add new layers to organize data, mapping the incumbent system to a non-relational database. In this manner, you get to have your cake and eat it, engaging with cloud development, scalable performance, and all those benefits without destabilizing transactional systems.

The EP Imperative

EP, delivered at scale, is no longer a future business goal. It’s a current, pressing need. Whether you consider it for marketing, easing customer choices, reducing churn, operational safety (curbside pickup) or even use cases beyond individual shoppers — such as managing dynamic catalogs — EP and the data dynamism that underpins it are vital for digitally-empowered and maturing businesses.

A large body of research on the topic reinforce EP’s importance:

  • 1% of your customers are worth 18 times more than the average customer. (RJ Metrics)
  • 91% of consumers are more likely to shop with brands that provide relevant offers and recommendations. (Accenture)
  • Fewer than 10% of Tier 1 retailers believe they are highly effective at personalization. (Gartner)

My mobile carrier gets a nudge in loyalty and appreciation when my phone lights up with San Francisco Giants news on a lonely ferry. In a connected world, it scores a point with me. Consumer behavior in COVID-19’s wake amplifies the value of such context. How we capture and use data appeals directly to those new behaviors, using Extreme Personalization to chart the way.

Feature image via Pixabay.

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