Unlock Hidden Insights From Your Customers’ Purchase History

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Building a Clear View of Your Customers

The infographic highlights the key challenges faced by businesses as they create a customer view through unified data, analytics, and more.

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“History doesn’t repeat itself, but it often rhymes.”

This popular aphorism, commonly (and perhaps inaccurately) attributed to Mark Twain, is often referenced to support the notion that although prior events are not always a clear indication of things to come, they certainly provide valuable context. Nowhere is this sentiment truer than in the retail industry, in which a consumer’s purchase history holds valuable clues about their future buying patterns.

The Value of Social Sentiment

The concept of using a customer’s purchase history — sometimes referred to as order history — to get a better sense of who they are is nothing new, but what that history looks like, how retailers collect purchase history data, and how they extract insights from it has evolved in recent years. For example, product reviews have always been a valuable source of information for retailers but, with the rise of social media, we’ve seen a growing trend in the influence of social sentiment. Compared to product reviews, which strictly evaluate the quality of the product, social sentiment addresses the entire customer experience. For example, if a customer encounters a rude sales associate in-store, they might share that experience on Twitter or Instagram; put off by the negative experience, that customer’s followers might choose not to patronize that particular store or even the retailer as a whole.

Millennials and Generation Z spend more time online than previous generations and, as a result, are more likely to be influenced by social sentiment than their predecessors. In fact, 84% of millennials report that user-generated content from strangers has at least some influence on what they buy. Similarly, 54% of Gen Z considers social media to be the top influence channel, surpassing retail websites.

In order to earn the loyalty of younger generations of buyers, as well as to better understand their preferences and behavior, retailers need to turn their attention to social media. By keeping tabs on what’s trending on platforms such as Twitter and Instagram, retailers can gain insight into what their customers are talking about and how they’re talking about it, and then use that information to be more agile and proactive. For example, had athletic apparel retailer Lululemon noticed sooner that its yoga pants were trending on Twitter, it might have gone back to its manufacturers sooner, thereby avoiding the see-through yoga pants debacle of 2013.

Retailers should also consider investing in social media monitoring platforms such as Sprinklr and Hootsuite, which make it easy to analyze customers’ social postings and assign them sentiment value; based on that value, a retailer can determine whether a customer is an influencer or a follower, which allows for more granular customer segmentation and adds nuance to that customer’s purchase history.

How Purchase History Has Evolved

Speaking of how purchase history has evolved, let’s talk about the means by which retailers collect and analyze customer purchase history data. Traditionally, retailers have partnered with third-party firms that have greater computing resources to generate purchase history reports. What’s exciting is that now, thanks to technology, retailers can do much of this work themselves by pulling information from internal sources, such as their business’ point of sale (PoS) system and eCommerce solutions, and external sources, such as a credit card database.

As far as internal data is concerned, there are six types of purchase history:

Unlock Hidden Insights From Your Customer’s Purchase History

Each of these transactions generates substantial purchase history data that retailers can analyze to extract valuable insights about their customers, as well as to identify different data trends, such as:

  • Customers: Using purchase history data, retailers can see who buys what and when; this gives retailers a clear sense of where customers are most likely to shop and when, what types of marketing content they might be interested in, whether they’re likely to be repeat customers, and so on.
  • Dates: Purchase history data can indicate when products are ordered, and at what volume. Based on this information, retailers can recognize seasonal selling patterns — for example, a retailer might see a consistent increase in the volume of sales in the months leading up to Christmas, and a sudden drop off immediately after the holiday season ends. In addition to seasonal trends, as technology becomes more advanced, retailers are also able to use purchase history data to gain up-to-the-minute insights and make changes to pricing and merchandise accordingly in near-real time. This last item is especially valuable because even the span of the day can change a decision entirely.
  • Products: Purchase history data can show which products are the most popular, and which products are commonly sold together. This information serves as a key input for recommendation engines, which can suggest which products need to be kept in stock at all times, which products sell best when displayed together, and which products could potentially be bundled and sold at a discount.
  • Discounts: Speaking of discounts, retailers can see which coupons, discounts, and deals generate the greatest rate of return by analyzing customer purchase history data. Retailers can use this information to better understand how customers are incentivized and what kind of messaging is most appealing to them in order to build more successful marketing campaigns in the future.

The Benefits of Analyzing Purchase History Data

Retailers can leverage purchase history data to enhance the customer experience by:

  • Upselling or cross-selling based on custom product recommendations. Seeing which products a customer has purchased in the past or which products they frequently repurchase can present valuable upselling and cross-selling opportunities.

    Example: An appliance store might filter purchase history data to see which customers recently purchased a washing machine; from there, the retailer might drill down even further to customers who recently purchased a washing machine and have two or more children. IoT sensors on the machines, themselves, could also indicate that customers within this demographic typically wash multiple loads of laundry per day. Based on this information, the department store might send those customers a targeted marketing campaign advertising a service that delivers laundry detergent to their doorstep every few weeks.
  • Delivering personalized messaging and marketing. Retailers, take notice: 76% of customers are interested in receiving personalized discounts based on their purchase history, while another 59% who have experienced personalization report that it has a noticeable effect on their purchasing. Retailers must use purchase history data to understand what matters to their customers and act accordingly.

    Example: A cosmetics retailer might notice that one of its customers has a preferred brand of mascara. Based on that information, the retailer might send that customer a personalized email asking whether they’re running low on that product, as well as a coupon or discount code that they can use to repurchase it. While repurchasing the mascara either online or in-store, the customer might even put additional items into their cart, increasing the overall profit from the sale.
  • Forecasting future demand with greater accuracy. As mentioned earlier, retailers can use purchase history data to see which products are the most popular, which ones are the least, when certain products are most likely to sell, and so on. This information enables retailers to engage in more data-driven decision-making, such as deciding which merchandise to restock based on projected future demand or coming up with strategic ways to get slow-moving inventory off shelves.

    Example: Using customer purchase history, a sporting goods store might see that they’ve consistently sold out of a particular model of a mountain bike over the past six months; they might also see that they’ve sold relatively few units of a different model of mountain bike in that time. Based on this information, when it comes time to restock inventory, the retailer might double its order of the popular mountain bike model and choose not to restock the less popular model. Additionally, the retailer might move the slow-moving bike to a different store or region that has reported higher sales for that particular model.
  • Boosting customer loyalty. In order to earn a customer’s loyalty, you need to first understand who they are. Oftentimes, loyalty rewards programs are designed in such a way that they reward customers for money spent but fail to account for how customers spend their money. Using purchase history data in conjunction with customer segmentation, retailers can see which customers bought which products, and offer personalized rewards based on those customers’ interests.

    Example: After reviewing their purchase history, a video game store noticed that not only is one customer a frequent buyer, but also that they exclusively purchase Xbox games. Based on that information, the retailer might correctly surmise that the customer owns an Xbox console and therefore offer them loyalty deals on Xbox merchandise as opposed to, say, merchandise for the PlayStation or Nintendo Switch consoles.

A Note About Master Data Management

The entire point of analyzing purchase history data is to create a connected customer experience but in order to achieve that, your data needs to be connected, too.

Despite how far technology has come, many retailers still use multiple disparate systems to process and store customer information, such as PoS systems, customer relationship management systems, survey systems, and so on. For example, a customer might register online, shop at multiple brick-and-mortar locations, and place a catalog order, all for the same retailer. Since that customer’s information is now spread across at least three different systems, it might appear as though that information represents transactions for three separate customers, but that would be inaccurate.

This is where master data management (MDM) comes in. MDM refers to the process by which different business units and departments across an organization work together to:

  • Review information spread across multiple data sources,
  • Ensure that that information is consistent (and update it if it isn’t),
  • And consolidate that information within a single database.

By implementing a solid MDM strategy and storing all purchase history data within a centralized repository, retailers can gain a truly holistic view of their customers.

Learn From History With Hitachi Solutions

Making sense of the massive quantities of purchase history data that customers generate on a daily basis can seem a daunting challenge, even for retailers with access to the latest and greatest technology. Sometimes, it helps to have a little backup.

Here at Hitachi Solutions, we’ve been helping retailers craft exceptional customer experiences for years. In order to do so, we leverage the full power of the Microsoft stack, including Azure Databricks, Machine Learning, Dynamics 365 Customer Insights, Dynamics 365 Commerce, and Synapse Analytics. Whether you’re interested in learning how to extract insights from your customers’ purchase history data or how to be more efficient with data science, Hitachi Solutions can help. Contact us today to let us know what we can do for you.