CUSTOMER OBSERVER

Unlock Value in Loyalty Marketing

You are a loyalty management company. Providers have traditionally used your platform to build customer loyalty and promote products to customers. But they’ve left plenty on the table because, to date, across-category buying patterns have little to no analysis at all.

This is where Customer Observer unlocks value. Imagine the ability to analyse your customers’ purchasing decisions, all of them, by amount and frequency across all categories and all providers, and then to create customer groupings from that data.

For example, you find that customers who spend over $10,000 annually in hardware also tend to buy premium kitchenware, watch movies in Gold Class, buy premium fuel and buy golf equipment at sporting goods stores.

Now you can promote premium fuel by offering that group loyalty rewards of Gold Class movie tickets when they buy it from an associated brand. Or you can offer a voucher for premium fuel when they reach certain loyalty limits.

It’s highly targeted offers like this that drive purchasing decisions and upsell. And it’s possible because the machine learning and AI capabilities of Customer Observer can powerfully and precisely examine an immense number of products and purchase combinations. Traditional ad hoc reporting or “hunch-based” marketing cannot look that deeply into your transaction data and cannot compete with its results.

Granular Marketing for a Trans-Tasman Consumer Electronics and Appliance Retailer

Meet our client, a high-profile retail chain with over 50,000 customers in its loyalty programme and stores in Australia and New Zealand.

It has invested heavily in television and print campaigns and has tried electronic marketing with limited success. It also has years of transaction data identifying high spenders, best-selling products, and the highest-earning stores, but data on why customers buy and their relationship to a brand’s product, location, and promotions is lacking.

Customer Observer can turn that data into sales. Using advanced machine learning and AI, it analyses all customer transactions by product category, sub-category, location, day/time and sales method to accurately identify purchase patterns. It will then use logical associations to determine the common behaviour factors and create customer groups.

At this stage, Customer Observer has already unlocked value in the database by helping the company truly understand its customers’ behaviour and showing it how to match marketing spend at a granular level to that behaviour.

The final step is the Holy Grail. Using advanced decision tree analysis and machine learning, Customer Observer can move on to accurately determine the next product each customer is likely to purchase. This provides the source data for potent individualised marketing campaigns that can at least double adoption and response rates.

Online Shopping Business Boosts Sales

With more than 700,000 items in its online catalogue and visitor traffic climbing steadily, this New Zealand-based online retailer has plenty going for it. But there’s a downside: too many visitors are browsing without buying.

Fixing it requires the Customer Observer from DSS. Rather than promoting products on a best-guess basis, it starts with the question: what do the information we receive from cookies, referrals and sign-up profiles tell us about what each customer is likely to buy?

Customer Observer harnesses the immense processing power of machine learning and AI to analyse all customer transactions and even site traffic data to segment customers and derive a market basket specific to each one. Next time they visit the portal, they are presented with offers that they are statistically more likely to be interested in. It’s a personalised shopping experience that increases the site’s relevance to the visitor and boosts sales on a per-customer basis by between 15 and 20 percent.