Businesses that operate brick-and-mortar retail locations increasingly rely on customer intelligence data to help them make better decisions. But getting more value from your customer intelligence efforts isn’t simply a matter of getting more data. Instead, there are multiple factors you should consider when expanding your program.

In this article, we’ll show you what we’ve defined as the four pillars of a robust customer intelligence strategy. After reading, you’ll have a better sense of what your business already does well and which areas are most appropriate to pursue next.

But first, let’s discuss why your business should pay attention to customer intelligence in the first place.

Customer Intelligence Is Key To the Experience Transformation

As brick-and-mortar retail continues to evolve, one trend is abundantly clear: experiences rule. Physical retailers must increasingly position themselves to provide unique experiences and utility. And they must shed the perception that their physical locations are merely places to conduct transactions.

Brands that have already begun to make this transition have shown promise. Recent examples include shopping malls adding fitness centers to their tenant mix, boutiques that completely revamp their stores every few weeks, or big-box retailers that provide live events for customers.

But making the right decision about how to change the customer experience can be a real challenge for brick-and-mortar businesses. E-commerce businesses have the benefit of being able to iterate rapidly on the customer experience. But retailers with physical locations don’t always have that luxury.

That’s why a robust customer intelligence strategy is key. Without the insights to understand who your customers are and what they care about, you run the risk of making major investments to the customer experience that don’t improve business outcomes.

Now that we’ve established why customer intelligence is increasingly important, let’s explore the four pillars of a robust customer intelligence strategy.

Customer experience at Mall of America

Live music at Mall of America created a unique customer experience – exactly the kind that can’t be replicated online. (Photo credit: Colleen Lanin)

4 Pillars of a Robust Customer Intelligence Strategy

We’ve identified the following as key attributes of a robust customer intelligence strategy:

  1. A healthy data culture
  2. Data on the entire customer journey
  3. A single system of record
  4. Diagnostic and prediction expertise

Some businesses have already integrated all of these pillars into their customer intelligence strategy, but if you haven’t, keep reading to gain a better understanding of where you might want to focus next.

1: A Healthy Data Culture

Access to the data itself is one thing, but in order for insights from that data to change the course of business, your company needs to have a healthy data culture.

What does having a strong data culture mean? That your team consistently uses data as a part of its everyday decision-making process. Data is only as good as your team’s willingness to seek insights and rely on that information to make better decisions.

According to research from McKinsey, “the emergence of data analytics as an omnipresent reality of modern organizational life means that a healthy data culture is becoming increasingly important.”

Becoming a data-driven company involves much more than simply buying the right technology. Training, governance, processes, and of course, execution itself are all necessary components of a healthy data-driven culture.

Companies with a strong data culture will also understand the importance of a solid data policy as well. With so much recent poor oversight on customer data, protecting private information from misuse is more crucial than ever.

The reason we’ve listed this one first is because having a strong data culture is so key to everything else. Data is only as good as your willingness to rely on the insights it can provide. In fact, you could argue that’s it’s better to have a data-driven culture (but little data to work with) than a weak culture with plenty of data at hand.

Next steps: A discussion of how to create a data-first business culture is outside the scope of this article, but if you believe this is a barrier your company needs to push through, take a look at these articles from McKinsey and ZDNet

2: Data On The Entire Customer Journey

Consumers don’t encounter with a brand within a single context. Instead, they interact with your brand in a variety of places, both digitally and in the physical world.

That’s why obtaining data from a variety of sources and channels is so key. Without it, your business may have good intelligence on a particular aspect of the customer experience, but it will lack a holistic view of the customer journey.

Businesses seeking this holistic view may incorporate the following into their customer intelligence strategy:

Advanced business may even go a step further and incorporate data from sources that influence customer behavior, like news, weather, and tenant mix.

While all of this data is useful on its own, it can be difficult to see how everything fits together when your data lives in different silos. That’s why the next pillar is also so key.

Customer surveys inform customer intelligence.

Using Wi-Fi to connect with customers is a key component of customer intelligence in brick-and-mortar retail.

Next steps: start by talking to your managed services provider, IT department, or systems integration partner. Chances are you’ll need help in deploying wireless networks, calibrating location analytics software, and WiFi analytics software. Your marketing agency can also advise you on which digital data sources may be good next additions.

3: A Single System of Record

Bringing your data together in a single system of record allows you to make an important shift. Instead of looking at customer intelligence data in silos, you can now develop a more holistic view of customer behavior.

To get there, you’ll need specialized software that can integrate with all of your disparate data sources and aggregate it together in a user-friendly way. This is especially key if your business operates both physical and digital spaces.

For example, an airport might look at how customer sentiment varies based on the gate that passengers spent time in. A shopping center might explore how social media engagement relates to revenue at its fashion tenants. Or a retail chain might see how weather impacts visitor count.

The possibilities for gaining new insights through a single system of record are myriad. Without it, you might overlook key insights or may have to spend hours manually cross-referencing data.

Next steps: to bring all your data together in a single system of record, you’ll need software that can integrate with the data sets that are unique to your business. Not all BI and analytics software is created for the same use cases, so spend time finding a solution that fits your needs.

4: Diagnostic and Predictive Analytics

Much of what we’ve described so far involves “description” –  a look at what happened at your business in the past. But “description” is just one rung on the data analytics ladder.

Diagnostic analytics helps you understand “why” something happens. For example, a shopping center might use a diagnostic analytics approach to identify why a long-term tenant saw a recent drop in foot traffic. Or a shopping center might look at what social media campaigns drove shoppers to visit its stores.

Predictive analytics goes a step further and helps you forecast what may happen in the future. It often makes use of technologies like machine learning and artificial intelligence (AI) to recognize patterns. An airport looking to forecast congestion within its terminals might use predictive analytics to see how weather affects passenger journey times.

Despite the benefits for diagnostic and predictive analytics, even the most data-rich businesses may find these disciplines a challenge. That’s because exploring the relationships among all these data sets takes special expertise. Not to mention the time necessary to do the research and analysis.

Because of those challenges, businesses making use of diagnostic and predictive analytics have usually hired a data science team – either in-house or through a consultancy. Data scientists bring together statistical analysis, research, programming, and domain expertise to develop the right approach to solving specific questions.

Data scientists can add significant value to your customer intelligence strategy.

Data scientists can add significant value to your customer intelligence strategy.

Next steps: To incorporate diagnostic and predictive data into your customer intelligence strategy, you’ll need to build out a data science team of your own or bring on a consultancy who can help you get started. These resources from VentureBeat and Entrepreneur provide good starting points on the former.

How Quickly Does Your Business Need to Pursue Customer Intelligence?

As brick-and-mortar businesses work on making the transition from “transaction-driven“ to “experience-driven” places, there are two threats they need to be aware of.

The first is from other brick-and-mortar businesses. If your business is a laggard, then you risk being left behind by competitors who are better prepared to invest in experience-driven locations. And more likely than not, these same businesses will make customer intelligence a key part of that process.

The second threat is of course from digital businesses themselves. More and more businesses that were previously purely digital – from Warby Parker, to Amazon, to Bonobos – are starting to build out presences in the real world. These digital-first businesses are usually “data-first” businesses as well.  They’re far more likely to have an advanced customer intelligence strategy that informs experience design in both the digital and the physical world.

The goods news is that it’s easier than ever for brick-and-mortar retailers to rival digital businesses in data sophistication. The decision brick and mortar businesses should be making is not whether they should invest in customer intelligence or not, it’s how aggressively they should go about it.

Interested in improving your own customer intelligence strategy? Skyfii’s customer intelligence software and data consultancy team have helped thousands of business do the same. Click here to get in touch with us