By Kristian Nordtømme, Chief Commercial Officer
Calculating the revenue generating value of customer data is a very straightforward exercise if you’re a retailer. Retailers can look at factors like Customer Lifetime Value (CLV), purchase frequency, and amount of average purchase, to determine the revenue they generate or could potentially generate from their customers. There’s a reason almost every successful retail brand today invests in continually engaging and growing their loyalty membership…first party data. And first party data is getting more valuable every day.
In fact, according to a study by Deloitte Data Valuation: Understanding the Value Of Your Data Assets
“Increasingly, data assets are the engine driving the total value and growth of modern organizations. As a result, building a framework to discover and realize the potential of your data is critical to increasing the value you provide to shareholders, and to optimizing the future success of your organization.”
For retail real estate owners and management groups the lack of transaction data makes determining the value of customer data a little more complex, but by using some generally accepted industry averages, and our client database knowledge, we can get reasonably close. But before we look at revenue generation, let’s look at some other areas where shopper data brings value to retail real estate.
Customer Acquisition Cost
Finding and attracting new customers can be costly. A new era of increasing data privacy standards and consumer empowerment means the heyday of third-party data collection using cookies is coming to a close. As third-party data becomes more complicated to collect it is making it more expensive, in turn increasing the cost of Pay-Per-Click (PPC) advertising. In a recent survey of marketers 44% of respondents believe they will spend 25% more on PPC than they did in 2021 to achieve the same results. The good news? This also increases the value of first party data assets too.
First party data is free to collect and most experts in the field agree that first party data is more accurate since it’s obtained directly from the customer. It’s also a more future proof method for data collection, and with the requirement for consumer consent and company transparency it is considered the most ethical. Existing customer data can also be used to improve targeting, and find more new customers that “look like” your current shoppers.
Improving Customer Satisfaction & Retention
The more you know about your customers, the better customer experience you and your tenants can provide. The better the experience the longer your customer is likely to stay increasing the potential for cross shopping and higher expenditures.
Market Research and Insight
First party shopper data can provide valuable insights into customer behavior, brand preferences, and market trends and these insights can guide strategic decisions, like merchandise mix, and improve the outcomes and ROI of marketing campaigns, events, and promotions.
Asset Optimization
There are formulas to help quantify market share growth or erosion, identify potential risks and understand the value of a particular customer or set of customers, but without customer data we have no baseline data to build those formulas from.
And Finally Revenue Growth
Revenue growth can be achieved by increasing the visitation frequency, basket value or a combination of both. For retail real estate ownerers we very often see that the most low hanging fruit in terms of increasing revenue is to increase total dwell time and average number of stores and restaurants visited each time the shopper arrives at the shopping location.
With a shopper database where you know both the demographic attributes and the actual preferences of the shopper based on previous behavior, in combination with the right digital tools, this allows you to provide personalized digital communication that inspires and incentivises the shopper to visit more stores, increasing both conversion and total spend.
For example we can use a shopping centre from one of our client portfolios as a starting point. This grocery-anchored centre has a shopper database with around 70 000 profiles which equals around 65% of the unique annual visitors (unique wallets). During an average year the centre sends around 2 million personalized digital messages that are focused on unique benefits and offers, which are only available to registered profiles of the shopper database. This leads to around 145 000 documented in-store conversions per year.
Applying an average basket value of 25 EUR per conversion this represents 3,6 million EUR in incremental revenue for the centre per year. Breaking this down to the individual profile level makes the average annual value of each shopper in the centre database 51 EUR. Multiplying the annual value of an individual shopper database member, by the conservatively estimated lifetime shopper patronage of 10 years, assuming no net database growth, makes the estimated lifetime database revenue contribution for the centre 35,7 million EUR!
Want to know more about how shopper data can help you increase revenue? Let’s Talk