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Oracle Retail Blog helps retailers stay current on customer successes, hot retail trends and industry best practices.

How To Shift From Knowing To Understanding Retail Customers

Mark Lawrence
Solutions Director, Analytics and Enterprise
This is a syndicated post, view the original post here

In case you missed my recent article published on Retail TouchPoints you can read it here.

The world around us is changing rapidly in light of the current situation, however, modern retail principles will remain key, and retailers should continue to keep the customer at the center of everything they do.

Retail marketers spend a lot of time collecting and analyzing facts about their customers. What they like, where they linger online, when they click, what they add to their carts. These data points help to predictably know customer habits, preferences and price points.

Over the last 10 years, developments in data analytics have been especially useful in assisting marketers in extracting maximum value from retail customer and consumer data. Still, now it’s time for brands to make the shift from knowing their customers to truly understanding them.

What’s The Difference?

Knowing simple, predictable facts is helpful for business planning, but having a whole landscape of contextual information lends an understanding that can help marshal a precise and internally coherent marketing strategy.

It’s the difference between knowing that someone makes a particular purchase at a specific time of year and understanding why they make it. Understanding requires contextual intelligence, and allows brands to pre-empt changes in behavior, anticipate adjacent preferences, and predict and influence future interactions.

You might know that Customer A orders wine each week consistently, is also a cheese connoisseur, and possesses an array of other attributes that are common among foodies. This information could provide retailers the context necessary to promote and advertise complementary items — like a new charcuterie tray or set of wine glasses to go with their next indulgence.

The Multi-Dimensional Shopper

This is where third-party data comes in to help you build out — and understand — your customers in a multi-dimensional way. Consumer insights gleaned from interactions beyond your brand can bring critical new details into the mix and enrich what you already know. This could be as simple as more detailed demographic information, or it could also be valuable psychographic data about shopper behaviors. Are they holiday spenders? Part of a large family? Do they watch Netflix? It’s about piecing together who your customers are and recognizing them better through added context.

It takes all of the pieces of the jigsaw puzzle — from first-party web and app engagement, transaction, and loyalty data through to third-party enrichment — for brands to develop a good understanding of their existing customers. With that understanding in mind, retailers can then identify their next targets for new customer acquisition.

The concept of data enrichment is certainly not new. Still, the principle of applying contextual intelligence in a meaningful and real-time way can give retail marketers a significant leg up in ensuring maximum return on marketing investments. By enriching first-party data with external contextual insight, brands can identify interdependencies, relational synergies and new segments for more holistic personalization efforts. The next step is putting this insight to work.

Put Your Understanding To Work

Enriching owned data isn’t just about building out individual profiles, but developing a more comprehensive, in-context perspective of entire ecosystems. Third-party data can help determine details on household members and roles, and how they might influence one another in terms of products and preferences. Similarly, extra color and understanding can be gained from device activity data.

Consumers use their devices in different ways — perhaps someone views ads on their phone but makes their purchases on a tablet — and data insight can now link these activities seamlessly to give a more fluid and granular perspective of the browsing and buying experience.

Affinity analysis can also uncover hidden correlations between items frequently purchased together, and how promotions impact their basket buddies for better or worse. Combining this level of understanding with ad tech and activation can help retailers execute granular promotional strategies. Other patterns may also reveal themselves, like how customers redirect once a favored product is out of stock (also referred to as demand transference), and how consistent that shift is — knowing everyone’s second favorite choice is invaluable for retailers looking to save the sale.  

The Implications

Coming to understand consumers better through a combination of owned data with enriched, contextual intelligence will soon be best practice for brands looking to extend their reach and avoid wasting marketing spend on suboptimal results. But this isn’t just about extending reach. It’s about gaining a new, multi-layered understanding of existing customers to meet their needs more fully while making sure they’re acquainted with the right products and services from your range, with messages delivered on the right channel, at precisely the right time.

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