Posted on: December 11, 2019 | 5 min read

How Analytics Can Uncover Your Customer's Holiday Spending Habits

Without a customer-centric focus, companies will not be able to survive. We’re living in a time where consumers have a plethora of options and substitutes for the same products and services that you offer, and one of the best ways to differentiate yourself amongst these threats is through your organization’s customer experience. Considering that “64% find the customer experience more important than price when purchasing something”, there has never been a more crucial time for organizations to begin to unlock insights about their customers through data and analytics.

Developing a complete view of your customer allows you to understand not only what’s working and what’s not but to hopefully uncover information on how to enhance the customer experience. At CCG, we’ve found that having data and analytics you can trust in an easy to understand reporting format can have a transformational impact on your business - allowing for employee’s cross-departmentally to gain trusted and transformative insights by understanding their customers’ behaviors. We’ve also found that creating common retail analytics scenarios can allow for these immediate insights that further the understanding of your customer.

In this blog, we will review some of these common retail scenarios that, when explored, allow for a deeper understanding of your customer, and when acted upon, can provide a heightened customer experience.

Adapting to What Your Customer Data is Telling You

It is no secret that customer data has become a crucial component to most organizations’ strategies and that those who have not successfully utilized their customer data have reaped the consequences of doing so. Some organizations prosper when they are tasked with adapting to new circumstances, changing landscapes, or innovation within their industries. Others, however, find themselves at a crossroads between adaptation or staying in their comfort zone and choose the latter. Let’s take Blockbuster, for example, a once-dominant player in the video viewing market, who at its peak had almost 90,000 employees globally, and a little under 10,000 stores, now has one remaining open store. Why? Because when confronted with a changing digital landscape, one of on-demand streaming, ease of use, and greater variety, Blockbuster didn’t change its business model to fit the needs of its customers. As Russell Walker, author of “From Big Data to Big Profits: Success with Data and Analytics,” detailed “[Blockbuster] missed out on the opportunities that came from customer data and providing customers with value and added convenience.” Although they launched a website towards the end, it fell short on the analytical capabilities that their competitors possessed, and many stores were forced to close. As their former Marketing Communications Leader stated, “Digital would have changed Blockbusters business, for sure, but it wasn’t it’s killer, that credit belongs to Blockbuster itself.”

Although a lot of misjudgment lead to the demise of a once prosperous monopoly, there are plenty of things we can take away and learn from as a contemporary organization. The main takeaway being, we as organizations, need, more than ever, to adapt to what customer data is telling us. How? One way we’ve seen organizations have success is by asking questions to common retail scenarios like the ones below.

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Analyze Spending Migration of Customer Groups

One common retail scenario that can be used as an exercise to gather immediate insights and benefits is visualizing the customer migration journeys. For this exercise, lets narrow in on the customer migration journeys, specific to spending levels over time. Of the many things that this visualization can infer, one is ‘how to better identify opportunities and threats to specific spending tiers of customers. As companies, our journey maps can tell us what’s working, what’s not, and nearly everything in-between. Understanding this about our customers can allow us to not only increase retention and acquisition but to enhance the customer experience.

Example: Let’s use Trendy Corp and customers Alana and Lauren for this exercise. Alana’s customer profile has a relatively low spending level over time with Trendy Corp. After analyzing their spending levels over time, we’re able to see that Alana only makes purchases once every couple of months, and it tends to correlate to time-frames where Trendy Corp has sales on their most popular pair of glasses.  Lauren spends a significant amount consistently over time with Trendy Corp. After analyzing their spending levels over time, we’re able to see that Lauren makes purchases once or twice a month on a variety of different products. While looking at their migration journeys, we uncover that Alana almost always enters the desktop version of the website through googling “Trendy Corp’s glasses,” navigates to the “Sales” section of the site, and only makes a purchase when said glasses are found in the sales listings. Lauren enters the mobile version of the website every time she receives the monthly “Trendy Corp loyalty program email,” to check out what new items are being promoted. She tends to make a purchase off every email.

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Debrief: So how can knowing this enhance our customer experience? Analyzing different data points from our customers, such as the example above, allows us to make better decisions that should ultimately enhance the customer’s experiences with the brand. After uncovering the information exampled above, Trendy Corp could reactively consider increasing promotional emails to customers like Lauren and potentially target customers like Alana in an email campaign every time they put their sunglasses on sale. Why? Because according to Smarter HQ, “80% of customers are more likely to purchase a product or service from a brand who provides personalized experiences."

Explore Seasonal Nuances of Merchandise

Another common retail scenario that can be used to gather insights is exploring the seasonal nuances of merchandise. For this exercise let’s narrow in on understanding which categories of products are prone to seasonal differences in purchase behavior by geography, to better align marketing and merchandising efforts to the propensity of consumers to buy those categories.

Example: For this exercise, let’s continue with Trendy Corp. Let’s look at their stores in Massachusetts versus Florida for the following products in the month of November:

  • Beanies
  • Umbrellas
  • Swimsuits

Massachusetts is traditionally a colder area during the month of November, whereas Florida tends to be warmer with a significant amount of rain and storms. After looking at their data, Trendy Corp notices that during this time frame:

  • Beanies do particularly well in Massachusetts
  • Umbrellas do particularly well in Florida
  • Swimsuits that traditionally do well throughout the year in Florida don’t sell well in either location.

Debrief: So how can knowing this enhance our customer experience? After uncovering the information exampled above, Trendy Corp could reactively consider adapting promotions and marketing efforts for the two geographical locations. An example of how they could do this could be to promote the seasonal items favorable to each location during the month of November. For instance, perhaps Trendy Corp runs a campaign targeted at their Massachusetts customers, for a “sweepstakes to a tropical destination with a purchase of $100.00 or more of their Beanies.” Why? Because, “Consumers are 40% more likely to view items that are recommended based on information they've shared with the brand”, such as their geographical location. In addition to insights for marketing initiatives, this information could also tell Trendy Corp:

  • How best to organize products within the store based on these seasonal nuances
  • The quantity of products to order for each store per location and season

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The importance of customer-centricity is and should be at the forefront of every organization’s strategy. The most successful organization are ones that put their customers first in their decision making. As we’ve seen exampled in companies like Best Buy, if you’re not adapting to what the customers and your data and analytics are telling you, you’ll likely be left behind or substituted for an organization that is taking these things into account. For some, this process and shift in thinking may seem tedious or intimidating, but it doesn’t have to be. Companies like CCG can help assist in helping your organization to elevate its customer experience and 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 or get in touch with one of our experts.

 

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