Picture this, you’ve huddled with your Marketing team and agree that your next advertising campaign should target the customers you are at risk for losing. After rummaging through your spreadsheets, talking to IT about their recent financial dashboard and qualifying your own gut feeling, you come to realize that you can’t tell whether those “at-risk” customers are shopping in another department of your store, left all together, or are just using a different medium like online for their purchases.
It sounds all to familiar. You have plenty of data about your customers, but it is sourced in various systems and only gives you a portion of the picture you need.
By enhancing your customer intelligence maturity, your organization can better understand the patterns in the makeup of your customer base, purchasing habits, and frequency of visits. These concepts allow your retail business the opportunity to detect patterns to predict where time and effort should be spent strategically. Below are three scenarios that can provide immediate impact to your business.
One of the most fundamental concepts within customer intelligence is segmentation. Customer Segmentation clusters behaviorally-distinct groups to use as a basis for identifying opportunities and gaps within the customer base for most spending. The main concepts of segmentation are geographic, demographic, behavioral, and psychographic. Within a customer intelligence platform, you can use customer segmentation when looking at purchasing data.
Within Customer Segmentation, you can further illustrate Category Affinity. Affinity is the likelihood of a customer to engage with and favor a category or brand alongside a variety of other categories and brands. Depending on the detail needed, this analysis can be by department, sub department, and class. Learning from the patterns in their category affinity can lead to targeting cross category purchases. For example, if you know that 1,500 customers shop for Women’s Tank Tops, but have never purchased Women’s Jeans, then you might be able to develop a new campaign to target that spend. Overall, category affinity can beneficial for you to predict demand forecasting, see gaps in your product portfolio, and enhance targeting efforts.
Customer Health looks at ways that a customer base is attracting, retaining, and growing. For most retailers, this incorporates both marketing and customer services initiatives. The idea of Customer Churn is turnover, or when a customer stops using your products or services for some time. Luckily, with sales data and knowledge on buyer behavior, this concept can be predicted. An example of a churn model, which uses predictive analytics, is a One-Time Purchase Propensity. This model looks at a one-time customer’s likelihood to return to your business.
Understanding predictive churn can ultimately save your retail business both time and money. Here is a scenario: Plank Furniture sells home décor to customers at rates of every four months to a year. Initially, Plank Furniture set up their discounts and promotions to their entire customer base and new markets. By using a blanket approach to their customer base, however, there wasn’t much of an impact. Instead, by using Churn Propensity they were able to differentiate discounts across customers. By evaluating different customer segments, Plank Furniture was able to determine how to offer minimal discounts through digital advertisements to customers who were already deemed likely to buy. Then, they offered more substantial discounts through email to customers who were at a high risk of churning. In the end, Plank Furniture received higher sales and retention without increasing costs.
Through the above customer intelligence scenarios, you were able to identify your customer groups and understand who you are attracting, growing, and retaining. Now it’s time to figure out what channel would best allow for us to acquire said customer groups.
Channel Affinity allows retailers to develop a sense of each customer’s engagement within each channel. Examples of channels are direct mail, apps, email, and social media. A customer intelligence platform can enable you to combine analytics from social media and web analysis to see from the first touch to last touch attribution of a sale. Understanding your different customer touch points can enable you to see that the same person who bought online last week then used a direct-mail coupon in-store. These analytics can be leveraged using credit card data, CRM data, and membership/loyalty data.
By better knowing what channels attract which customers, you can analyze the Propensity to Convert. This analytics scenario leverages historical data on conversion ratios and success metrics from your buyers. Then, with third part data, create a lookalike audience with similar segmentation qualities. With conversion propensity, it’s assumed this lookalike audience will have the same conversion rates to the originally placed customer segments.
This concept allows you to predict customer likelihood to convert based on their favorable response to a marketing stimulus (email, direct mail, etc.) to create repeatable and successful processes.
Segmenting customers, monitoring retention, and identifying channel preferences can lend to immediate results on your business. By using concepts like segmentation analysis coupled with category affinity, for example, you can immediately see what types of units need more attention in both Minneapolis and Manhattan. Or, see what offer would work best through digital channels by using channel affinity and propensity to convert analysis. By leveraging these concepts, you can drive your research and development teams with better guidance and make use of the data that you’ve been retaining for years.
Elevate your customer intelligence with a platform that suits your retail organization, for more information about CCG’s Customer Intelligence (CI) for Retail, visit www.ccganalytics.com/ci.
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