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5 ways to improve customer service through data

To survive as a retailer in today’s highly competitive international market, top-tier customer service is vital. According to Accenture, 88% of customers use digital channels to make decisions about where and what to purchase, and a majority of these customers feel little brand loyalty a company who isn’t serving them (or people they feel are like them) well. If your customers are having bad experiences they are more likely to switch to a competitor, more likely to share bad experiences online, and worst of all, more likely to influence others to switch as well.

So how do you improve customer service? You’ve got to anticipate your customers’ needs, and, in turn, maximize customer behaviors that will keep them coming back. At Evo, we’ve discovered there are five effective ways to anticipate and maximize customer behavior— all using data!

1. Use Market-Basket Analysis to pinpoint keystone products drawing your customers to your business, so you can price them optimally.

Few retailers today compete on price alone. While some buyers may be motivated to choose a particular retailer due to all-around low prices, most consumers are drawn to a brand due to a particular product that differentiates them in the market. These keystone products have to be carefully priced to ensure they appeal to your target customer.

Sometimes this means ensuring keystone products are always affordable. A grocery store may regularly discount milk or bread to appeal to families who will do larger shopping trips motivated by highly visible low prices on staples. Luxury retailers, on the other hand, may make keystone products incredibly expensive and thus appeal on exclusivity. Ultimately, customer service carefully balances these needs.

Pinpointing and pricing keystone products at an optimal point is done through market-basket analysis. Of course, accurate results require large volumes of data. Your business must collect and understand its own data incredibly well in addition to handling millions or even billions of transactions daily in order to be confident that results are accurate.

For most companies, it’s helpful to have an outside expert contribute meaningful, relevant data to the algorithm. When we do market-basket analysis at Evo, we don’t draw conclusions without leveraging our full database of over 1.2 billion consumers— just to be sure that the sample size is large enough for an extremely high level of confidence. The more data, the better you can accurately identify and price those keystones. If your sample size is too small, outliers can skew your results, wrecking your ability to predict behavior accurately.

2. Eliminate stock-outs of popular and keystone products.

When people are confronted by sold-out products, the most common response today is to turn to a competitor. With the click of a button on a cell phone screen, your customers can buy a competitor’s product without even leaving your store. Stock-outs can cause damage beyond losing a single sale, however. Consumers today see the lack of products as a lack of service, which makes them less likely to turn to you in the future. 

Think occasionally running out of popular items is unavoidable? Think again! A Harvard Business Review study reveals that 72% of the time, poor company decisions, not outside factors, cause stock-outs. A data-based replenishment strategy can eliminate these issues. At Evo, we can even help reduce the 28% of stock-outs caused by supply chain issues out of our clients’ control by forecasting those problems using external data in our algorithms.

3. Use machine learning to make more accurate predictions of what your customers will want before you’ve sourced products or started production.

If you produce a line of products that no one wants, no amount of advertising will make up for the losses that come from failing to serve customer needs. Accurate demand forecasting based on historic data and future trends and buying behavior work together to ensure that your products are exactly right for the current environment.

Moreover, the right data granularity means you can make not just overall predictions but also store-level forecasts. You can ensure that the right products will serve the right local market. Customers will feel like you provide better personalization, something now considered integral to service. Meanwhile, you’ll avoid unevenly sold merchandise.  

4. Make strategic discounts that will motivate and please your customers, not reactive discounts solely motivated by overstocks.

Discounting strategy is complex. Offer too little as a discount, and your customers won’t feel they are getting a real bargain. You may end up with too much unsold inventory. Offer too much as a discount, and your customers may not value your products as much. You may lose out on full-price purchases while your customers wait for the price to drop. That’s where the data can help.

Data-based pricing strategies allow you to offer promos that will maximize profit without damaging your brand. You’ll be able to see the predicted outcomes for multiple promos at once and choose which outcome best serves your customers and your own objectives. In fact, the right promotion can function as customer service. By personalizing offers to stores, groups, and even individuals in response to data trends, you show that you care. Plus, discounting in a non-reactive way ensures that the discounts are still appealing, not desperate. 

5. Be transparent about your supply chain— and minimize its waste.

74% of customers today want to know how companies source their products and what impact the supply chain is having on the world. That means good customer service increasingly requires full supply chain transparency. Not only do retailers need to understand who is making their products, but they also must commit to responsibly getting those products on shelves or into homes as sustainably as possible.

While it may not seem obvious, using data to define when products are shipped and where within the supply chain helps to significantly reduce waste. In fact, we’ve seen our clients reduce waste by 25%-40% simply by allowing the Evo algorithms to manage replenishment and product allocation. Less supply chain waste brings higher profits, but it also helps you show customers that you share their values. In the current market, catering to customer values is just as important as any other element of customer service.

Companies can no longer promise excellent customer service simply by training employees to be polite and helpful. While these traits may always go a long way towards generating goodwill, data can take companies much farther. Anticipating customer behavior allows you to prepare the perfect service long before your customer ever browses your products. You’ll be able to maximize their choices at the same time as you build a well-deserved reputation for customer service.

The editorial team

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