How to Use Customer Analytics in Retail
This article will show the best ways to use customer analytics in retail. With five real life examples...
There are endless opportunities for leveraging customer analytics in retail.
At the same time, however, retail is host to constant digital disruption. This article will demonstrate five use cases for using customer analytics omnichannel.
Let’s jump straight in.
What is Customer Analytics?
To refresh our memories...
Customer analytics means capturing and analyzing customer data by leveraging IT- infrastructures, machine learning (ML), or AI.
Don’t Panic ML & AI isn’t the intelligent robot-future pictured in pop-culture. Many companies are using AI to facilitate gathering customer data.
In eCommerce, customer analytics gathers all customer interactions with your webshop including which device customers are using, which browser, demographics, etc.
Why do you need Customer analytics?
Understanding which channels drive purchase behavior will - you got it - drive purchase behavior. Knowing which channels your customers come from means you will be able to personalize their experiences based on previous interactions from that channel.
It’s that simple.
Having a unified data platform that churns out customer data analytics will allow you to:
- Track metrics,
- Bridge the gap between different data sets,
- Create customer intelligence from data-driven insights, and
- Streamline and optimize products and strategies for omnichannel retail.
Again, Don’t Panic, we’ll show you how to use customer analytics in retail below.
1. Segmentation & Targetting
Managing your Marketing
For starters, customer analytics can be used to segment your audience according to their demographics or psychographics. This division of customers allows you to see how people are purchasing across different countries and even across values or interests. If there is a difference in purchase behavior between those groups, then you can tailor products and campaigns to engage better with each segment.
So, what is targeting?
Targeting uses data gathered from customer analytics so that you can create audiences that are served specific campaigns or ads in real-time.
Customer analytics can also determine where customers are leaving the funnel and why, where they spend the most time, which products they abandon in carts, and which products or messages makes them convert.
But that’s just the beginning.
For all you savvy salespeople out there, customer analytics helps predict customer lifetime values, measure ROI, allowing you to score leads.
Brick & Mortar Is Not Dead!
On the backend, the beauty of AI and ML algorithms for synthesizing customer analytics is that they can also be applied in-stores.
You can increase conversions by placing products that are frequently bought together physically closer to each other in-store.
Also, personalize the customer’s in-store experience by giving out coupons or baskets.
Body Shop does this by placing items that are frequently bought together in one basket and then discounting the overall product price.
Customer analytics also reveals CX pain points, which can be addressed in-store to provide a frictionless buyer’s journey and engage with people in the right way and at the right time.
Accordingly, you are also able to modify your in-store inventories based on your on-site visitors, which will save you time and money, increasing sales.
The bottom line?
Leveraging customer data for predictive analytics can be used to anticipate customer behavior, but also to measure and manage KPIs in support of broader company goals.
Expand and Grow Across Markets
“Going Local” may make you “Loca”.
We understand how difficult it can be when your global headquarters are across the world. What they deem as fit for CX may not reflect your experience in your market.
Thus, localizing will give you the upper hand, establishing you as a multilingual retailer and thus expanding your market presence.
Here’s how it goes:
First, start with a best-of-breed digital analytics solution that can produce customer intelligence so you can understand your local target market.
Second, gather data like demographics, cultural nuances, and language colloquialisms of this target audience.
Third, optimize your website for localization using this data.
For instance, this could be providing your local customers with a frictionless shopping journey taking into account local currency. Localization will thus ensure that your brand is kept consistent across different markets.
Innovative tech might work for Germans whereas Dutch people may want a more price-sensitive message that is a good value for money: these data-driven insights can be harnessed by AI and ML, and analyzed by your in-house data experts.
Interpro provides an eCommerce translation service, but it’s up to you to use customer analytics to define the first and second steps.
4. Product Attributes
Analytics From Marketing to Merchandise
Product attributes are descriptive one-word-wonders that elevate product features. Tags like “sustainable fabric”, “waterproof”, “real leather”, or “no chemicals” harness the power of analytics to better inform customers about what they’re buying.
Therefore: try experimenting with dynamic product badges!
This will also increase your search functionality or, in other words, how easy it is to find products based on its attributes.
This is important:
Using a mature analytics solution especially to optimize your products will reduce returns and allow you to centralize and organize your product information.
Oysho’s static product tags, for example, help customers understand the product more: “High Waist”, “Compression” etc.
Product attributes can also be in the form of bullet-points - see Amazon - or even Everlane, whose copy feels so personal and intimate it’s as if the product already belongs to you, i.e. “True to size. Take your usual size”, “Made in Montropoli in Val D’Arno, Italy”...
5. Ad Retargeting
“The majority of customer analytics is done on an ad-hoc basis with no clear idea why the analytics is needed and what the expected benefits will be” (Harvard Business Report).
This Harvard Study points to an issue that plagues most companies: how to make customer analytics actionable.
Well, ad-retargeting is one way to do this.
Looking beyond customer journey analytics and traditional marketing funnel considerations, ad-retargeting uses analytics to further understand and personalize the shopping experience for those special someones, serving targeted ads to shoppers who have been on your site.
Online advertising also reduces bounce rates whilst increasing conversion rates.
To sum it all up
There you have your five use cases of customer analytics in retail, applied omnichannel.
Your biggest ROI is improved customer experiences, so it is only natural that synthesizing customer analytics and performing it across all your channels and funnel-stages will improve CX and engagement.
Now it’s up to you.