Retail analytics is about using data to identify the factors that are impacting business outcomes. It also helps retailers evaluate strategies and understand why specific strategies are working or not.
It can also help uncover how customers behave, so you can track them across the store and understand where they want to buy.
Recent years have seen a surge in demand for data analytics in the retail industry.
In this post, we will discuss predictive analytics and its role in retail. But before that, let's understand the five forms of retail data analytics, each with its own strengths and weaknesses.
What is Retail Analytics?
It's a broad term used to describe the use of data, mostly from different sources or systems, to analyze and interpret performance.
In the retail sector, this may involve examining which stores are doing well and which are not, how online stores are performing, and what they should do next to get back on top.
Retail analytics is the new buzzword, but it can be tricky to pin down just what it means.
It's a broad term used to describe the usage of data for analysis. What's more, the term "stores" is misleading because it's just one way to measure performance.
If you sell through different channels, you'll want to measure them all.
Retail data analytics help you better understand your customers, enabling you to segment by behavior or demographics.
4 Types of Retail Analytics
There are four types of analytics:
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
What are the Four Types of Analytics?
The four types of analytics describe how data is used to understand the past, explain the present, and guide future decisions.
Here's a simple way to think about them.
Descriptive analytics tells you what happened. It looks at historical data to summarize performance. Reports, dashboards, and basic metrics like sales totals or website traffic fall into this category.
It answers questions like "What did we achieve last quarter?"
Diagnostic analytics explains why something happened. It digs deeper into the data to find patterns or root causes.
For example, comparing regions, channels, or time periods to understand why sales dropped or churn increased.
Predictive analytics focuses on what is likely to happen next. It uses historical data, statistical models, and machine learning to forecast outcomes.
Examples include demand forecasting, churn prediction, or sales forecasts.
Prescriptive analytics suggests what actions to take. It builds on predictive insights and recommends the best next step. This could be optimizing pricing, choosing the best marketing channel, or deciding how to allocate resources.
Express Analytics puts the voice of the customer at the heart of the business. Data alone does not drive your business. Decisions do. Speak to Our Experts to get a lowdown on how predictive analytics can help your retail business.
1. Shopper-level analytics: These metrics allow retailers to see how each consumer interacts with each digital or physical channel.
They can pinpoint shoppers' contact with the brand and find out whether those interactions were positive, negative, or indifferent.
This is the best kind of analytics because it lets retailers engage with customers in real-time. The brands that are seen as most responsive are most likely to gain customers' loyalty.
2. Transaction-level analytics: These metrics allow retailers to see the impact of marketing campaigns on both purchase intent and shopping behavior.
For example, a retailer might measure the success of an email campaign by counting the number of new customers who purchase from the retailer after receiving an email.
3. On-shelf analytics: These metrics focus on individual shelf items or categories of goods.
For example, these metrics might track traffic to competing products' websites.
4. Location analytics: These metrics provide a way to see how consumers in a particular geographic area engage with a brand online or off.
For example, in a retail setting, a retailer might track what percentage of shoppers walk in the door and what percentage use a mobile phone to purchase products.
5. Multichannel Analytics: These metrics allow retailers to see how consumers engage with a brand across multiple touchpoints—such as website, store, and mobile.
In a retail setting, this kind of information might help retailers understand how customers engage with a particular product line.
Benefits of Retail Analytics
Retail analytics gives retailers a more complete picture of customer behavior, product usage, and traffic patterns. This kind of information can enable retailers to:
Boost sales by changing store layouts, optimizing pricing strategies, or adding new products.
Gather valuable insight into which customers are making purchases and with what frequency.
Identify geographic areas that require increased marketing efforts.
Provide competitive intelligence involving compiling and analyzing data, especially on competitors and the industry as a whole.
For example, retailers might look at their competitors' websites to analyze the kinds of deals they offer and how they promote them.
Want to know how Predictive Analytics can help you reach your goals? --- Talk to Our Experts
5 Main Areas to Use Predictive Analytics in Retail
While data modeling has been traditionally used extensively in specific industries such as insurance and climate control, the one field where predictive data analytics can be utilized to its full potential is retail.
Retail, more so than any other industry, generates a lot of data.
Unfortunately, that same massive amount of data is also the problem with retail.
With so much data coming in, much of it in real-time, it isn't easy to manage, with a lot of that data never getting converted into insights.
To stay ahead in today's e-commerce age, retail merchants need to learn to handle incoming data and prepare it for analytics.
Any apathy in this means they are losing out on one of the most valuable uses of retail analytics – predictive analytics.
Predictive retail analytics is the proactive part of retail analytics.
In fact, some consider it to be a 'crystal ball' that can accurately tell you what customers may want next. Being able to tell what will happen with your customers can be the difference between dwindling sales and substantial revenue.
Before going down that route, however, here's a list of the kind of data that a retailer needs to have to leverage predictive data analytics:
- Point-of-sale data
- Consumer-related information, including that of loyalty programs
- Consumer demography
- Store and online navigation traffic flow
- Competitive intelligence
- Other external factors, such as weather
That certainly seems like a lot. So where does a retailer get all this data from?
- Website
- Smartphone app
- Loyalty programs
- Point-of-sale systems
- Supply chain systems
- In-store sensors & cameras
- Social media networks
So, in which part of their operations can retailers deploy predictive analytics to derive maximum value?
1. Personalization for customers
Understanding customer behavior and combining it with consumer demographics is the first step in deploying predictive analytics.
Retailers can use it to deliver targeted, highly customized offers to specific shoppers.
Before data analytics became mainstream, targeted offers were nonexistent or only available to large swaths of customers with one or two common characteristics.
But with the emergence of online shopping and data analytics, it is now possible to track behavior across channels, i.e., to monitor a shopper who researches in the digital store and then purchases the item in the physical store.
Such insights, coupled with retail predictive analytics, now enable merchants to make highly personalized offers to customers at a very granular level.
For example, retailers can personalize the in-store experience by offering incentives to encourage frequent purchases, driving more sales across all channels, and increasing overall sales.
This technique can be used to upsell or even cross-sell. For example, based on his previous buying history, we know John Doe has a fondness for buying brand X of chocolates at the start of every month.
Using predictive analytics, a retailer can now offer John a buy-two-get-one-free deal on chocolate.
Given how consistent his buying behavior is, John will likely take advantage of this coupon, resulting in more profit for the company.
2. Inventory and supply chain management
One area that is often neglected is the back-office operations.
Poorly maintained inventory is every retailer's nightmare. Supply chains need to be optimized to increase operational efficiency.
Predictive analytics helps answer questions such as what to store, when to store it, and what to discard and when.
Stocking up on slow-moving products or running out of popular ones are both problems. Such insights optimize performance and reduce costs.
Thus, predictive retail analytics removes this uncertainty, enabling purchases to be made simply on a hunch.
3. Customer segmentation & customer journey
A customer's journey is a map that tracks the buyer's experience.
It starts when the customer first contacts a brand and ends with a purchase order. The journey traces the process of engagement.
Contrary to popular belief, customer mapping does not end with the client placing an order.
It's also about a long-term relationship, mapping a customer's behavior after they receive their product.
The customer is at the center of every B2C and B2B company, and a map of the customer journey gives managers a ringside view of how customers or leads move through the sales funnel.
Data-driven insights can help retailers understand each customer's profile and history across channels.
You can monitor customer activity to determine who your best customers are and how they, and other good customers like them, behave and react to your retail marketing.
Predictive Retail analytics helps not only with customer targeting but also with customer segmentation.
Using affinity analysis, a retailer can cluster the customer base based on common attributes.
Merchants can use response modeling to examine past marketing stimuli and customer responses to predict whether a given approach will work in the future.
Churn analysis, on the other hand, tells you the percentage of customers lost over time and the potential revenue lost as a result.
Predictive analytics helps businesses predict a customer's lifetime value (CLV).
Retailers would like to know how to predict a customer's future value over the course of their interactions with the business
CLV forecasts the discounted value of a customer over time. CLV involves analyzing past behavior to determine the most profitable customers over time.
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4. Customer behavior or behavioral analytics
People-tracking technology has made it easy for retailers to analyze in-store or online shopping behavior and assess the impact of merchandising efforts.
Various consumer interaction points can provide data. These include social media, e-commerce sites, credit card swipes (transactions), and so on. Retailers today have access to diverse (and complex) data about their customers.
Using this and data points from earlier marketing and advertising campaigns, retailers can now build predictive models that link past behavior to demographics. Such models aim to score each customer based on the likelihood of their buying certain products.
This data-driven process also gives retailers invaluable insights into identifying their high-value customers, establishing CLV, a customer's motives behind a purchase, buying patterns, preferred channels, and so on.
Retailers armed with such knowledge can not only present personalized offers but also retain new customers. This is reinforced by loyalty programs that encourage them to buy from you over the competition.
But how do you retain those customers who used to be sure things when their loyalty is flagging? Using predictive analytics, retailers can gauge which customers are drifting and which have the potential to be long-term users.
5. Campaign management
Predictive analytics can be used to craft future marketing campaign strategies. The more you know about your customers, the more targeted your messaging can be.
Data-based decision-making reduces the number of decisions made on instinct or guesswork.
CLV can dictate where to focus your ad spend. This method can identify the channels and the times that require an increase in your marketing spending and resources.
To conclude, using data analytics no longer remains the sole purview of the retail biggies such as Amazon.
Thanks to the technology getting cheaper and more mainstream, predictive analytics can now be used even by medium and small retailers to be ahead of the competition.
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