7 Actionable Analytics for In-Store Customer Journey

Published by Ronny Max on

What’s In-Store Customer Journey?

In-Store Customer Journey | Behavior Analytics Academy
In-Store Customer Journey (Behavior Analytics Academy)

The In-Store Customer Journey is a quantified map of (anonymous) tracking of people & actionable insights on how to increase conversions and profits.

The Location & Time analytics create clarity on the Path to Purchase (Where), the operational and products Touchpoints (What), and the Buyers Behaviors (Why) in physical retail.

Here are In-Store Customer Journey Analytics you can start doing TODAY:

  1. Identify Purchase Points
  2. Map Path Trajectory
  3. Quantify Local Demand
  4. Prevent Friction Points
  5. Measure Engage Time
  6. Monitor Service Time
  7. Optimize Product Positioning

In 2019, cost-effective, accurate enough, and anonymous In-Store Customer Tracking is feasible.

Technically, the Customer’s Journey is mapped by Location & Time (x,y positioning) of objects detection and tracking.

Because the in-store tracking technologies capture the “people in motion”, the solutions generate performance metrics and actionable insights for Behavior Analytics.

(Pro Tip) To increase Profit and Conversions, we design Optimization Projects (A/B Testing in Physical Stores) that end with Actionable Insights!

Before we dive into the analytics of the In-Store Customer Journey

Here are 3 hard truths –

#1 The Retail Analytics Seamless Challenge: You Should Accept that Offline Customer’s Experience is NOT the Same as Online Shopping Behaviors

Despite the hype, there are distinct differences between online and offline shopping. The customer’s jiourney maps should be built based on how people behave in physical locations.

Here are the Top 3:

Nike’s House of Innovation (Source Ronny Max)
  1. Linear vs. Dynamic Maps: Online behaviors are dictated by how people move from webpage to webpage (Impressions and Clicks). Reality is a LOT messier. STOP thinking about Linear Customer’s Journey mapping.
  2. Sole vs. Social Visits: Online, people buy for themselves or for someone else. Regardless of the external and internal influences on the customer’s decision to buy, online visits are designed for sole individuals. the In-Store Customer Journey is Social.
  3. Conversion vs. Profit Goals: Conversion Rate Optimization (CRO) is a well-defined discipline for online commerce. Digital products are difficult and time-consuming to produce but easy to replicate – that’s why CRO exist. But physical locations have has unique costs and constraints. Conversion is super important. However, Profit is the secret to physical stores.

(Pro Tip) Segmentation and Personalization serve different business goals. With segmentation, you design the store. With personalization, you design promotions.

Our research found that one consequence of the new world of marketing complexity is that more consumers hold off their final purchase decision until they’re in a store – McKinsey & Company

#2 The Data Attribution Challenge: You Should Define Your Analytics either for Personalized Marketing or for Anonymous Customer Behaviors

In-Store Customer Tracking
In-Store Customer Tracking (Behavior Analytics Academy)

Another source of massive confusion is the difference between Personalized and Anonymous Customer Data.

For data analysts, this is a challenge of data attribution.

Here’s what you should know –

Location Marketing deploys location-based data that is tied directly to the identity of a specific individual.

Due to privacy laws, personalized promotions require the customer’s opt-in.

The catch, of course, is that the opt-in is often done to third parties such as Apple, Facebook, and Google. Even Snapchat.

Most retailers and brands are not so lucky…

Location Analytics depends on Anonymous People Counting (object detection) & Customer Tracking technologies.

People Counting solutions quantify the demand for a store, compare the sales potential of stores, and analyze the quality of proximity traffic.

In 2019, the advances in people tracking technologies allows us to move inside the store.

While retailers digitally interact with customers during the in-store visit, you don’t need the identity of the customer to optimize the performance of the store.

Because the objective of In-Store Optimization is to learn what happens before the payment point. The goal is to increase in-store conversions.

(Pro Tip) The Behavior Analytics framework classifies the Customer’s Journey into three core categories: Customer Engagement, Retail’s Calls to Action, and Friction Points in Store Operations.

Even at the highest levels, retailers don’t really understand either the rewards or the risks associated with anonymous vs. non-anonymous customer geo-location data.

RSR Research

#3 The Technology ROI Challenge: You Should Capture Your Retail Customer’s Experience as a Quantified UX Analytics

When building In-Store Customer Analytics models, you should first clarify your terminology and KPI definitions.

Tracking people generates raw data on indoor positing,

And successful data-driven retailers design stores based on the principals of User Experience.

For example, “Customer” and “Shopper” are generic terms in retail.

In Behavior Analytics, we used classify shoppers in three categories:

  • Visitor: a shopper in the store, from Entry to Exit points
  • Consumer: end user of the product (in/out of Shopping Groups)
  • Buyer: the person who pays (sole shopper or in Buying Groups)

How do you know how to classify a person?

It’s a judgment call

Based on data from tracking people

And Advanced Analytics helps

We can create a context for the customer experience and behaviors

For example:

  • Point of Sale (POS): the customer’s purchase history points to departments and products that may be of interest to a specific person.
  • Order Management: the tracking of products, categories, and brands points to popular purchases, specialized products, and odd items.
  • Assortment & Layouts: gives an indication of what is available for sale in the local store, and where is it located inside the store.

(Pro Tip) Start every in-store optimization project with three Cs – Connectivity, Correlation, and Communication. You should have a clear idea which data, which data sets, and who needs to know what and why, before you start working with the customer tracking technology.

Each time a consumer is exposed to an improved shopping experience, their expectations are reset to a higher level of experience.

Brendan Witcher, Forrester

Now you know that the methods of online customer’s journey are a starting point, however these maps requires a adaption to physical retail.

The next step is to combine customers and products data into an “actual” motion of people inside the store.

Here’s what you need to know –

In-Store Tracking of (Anonymous) People

How do we track a “behavior”?

To start we need a technical definition.

“Behavior” is quantified by Location & Time Position of Objects

Assuming we can identify and exclude the onsite employees, we track the (anonymous) customers by their activities in departments, aisles, and in front of products.

Regardless of accuracy, you should work with tracking technologies by:

Location & Stay Time (Behavior Analytics Academy)
  • Location (Virtual Zone): The virtual location of the object is defined as a relative position within an image, layout, or geo-location area
  • Time-Based Tracking: How a people tracking solution defines Time impacts the way we design the KPI Framework
  • Moving vs. Static Objects: Objects are tracked within a virtual area, in motion across virtual zones, or as static positions

(Pro Tip) 90% of technology projects fail not because of the technology, but because of process & communication. Amazing projects come from amazing teams.

While the business benefits vary, the following analytical frameworks can be deployed regardless of the technology, solution, and data quality.

In-Store Customer Journey Analytics You Should Start TODAY:

You are now ready to build a data-driven map of customer behaviors.

Let’s dive in –

#1 Purchase Points: Identify WHEN the Shoppers Decide to Buy & Boost Your Sales

The first in-store optimization project is actually the “one before the last step” in the physical store’s Path to Purchase.

When retailers think about the last step in the Purchase Funnel, they really mean the Checkout Process.

While the Checkout is the point where money changes hands, the decision to buy occurs inside the store. The location where people make the decision to buy – inside the store – is the Purchase Point.

“Purchase Point” refers to the WHERE & WHEN the shopper decided to buy the product

In-Store Purchase Points are distinctly different from Online Purchase Decisions. And even senior retailers can be confused about the differences between the checkout process and purchase points.

In-Store People Tracking - Purchase Points | Behavior Analytics Academy
Purchase Points (Behavior Analytics Academy)

Here are some examples relating to People Tracking:

  • In supermarkets, Purchase Points occur when a customer puts an item in the shopping cart
  • In apparel stores, often the Purchase Point occurs when a customer tries the shirt either in front of a mirror or inside the fitting room.
  • In digital fitting rooms, Purchase Point and Checkout are merged into a single step because the customer can do both with Smart Mirrors.

(Pro Tip) Purchase Points pinpoint the WHERE and WHEN visitors become buyers. By mapping the Customer’s Journey BEFORE the Purchase Point, we gain more clarity to the WHY people buy.

#2 Customer Flow: Map HOW Shoppers Move with Path Analytics & Design Better Stores

The In-Store Customer Journey cannot be described in a direct linear map, but we can do Path Analytics.

In-Store Path Analysis is about trajectory:

  • Entry: How people entered a specific virtual location
  • Exit: How people left a specific virtual zone
  • Frequency: How many times did a person stay/pass a particular area

Path Analysis provides the “Local Traffic Patterns” or “Local Demand”.

Here are 3 cases for Path Analysis:

  • Store Layout: Do people move around the store as you planned? The IKEA store, for example, take has a predefined shopping track. Do customers follow the predefined path? I usually don’t. Do you?
  • Department: Which retail sector is challenged in 2018? Department stores. Well… even small stores have departments – just think about Women or Men (Gender) or Kids Section (Shopping Groups), and so on.
  • Islands: In-store design we used to talk about Aisles. Today the topic is Islands (cases or displays in-store).

Path Analysis is a very useful method to understand the customer’s flow in any physical location, including retail stores, airport hubs, and shopping malls.

And if you watched over 1,000 hours of video like me, you know that people always surprise you. Always!

(Pro Tip) Path Analysis helps to understand the differences between normal and outlier customer behaviors. In context with Local Demand, you should learn more about HOW & WHY people search for a specific product.

Once we mapped possible in-store customer flows (what “we think” customers do), we turn to specific Touch Points

# 3 Local Demand: Quantify Number of Shoppers with Occupancy & Increase Product Demand

Occupancy is one of the most versatile and least appreciated metrics. It is used in workforce analytics, path analysis, and friction points. Practical analytics is to use Occupancy to define “Local Demand”

Occupancy counts the number of customers staying in a (virtual) zone, per period of time.

Local Demand” includes this analytics:

  • How many customers visit a virtual zone, per period of time?
  • Is the number of “Actual Local Visitors” above, below or normal?
  • What is the “Optimal Local Demand” (in context to conversion) for that particular virtual zone?

Occupancy is an ideal metric for quick A/B testing:

  • On special events (i.e. July 4th weekend) does demand changes for a particular product (i.e. meat and corn)?
  • What is the impact of assigning an expert associate (i.e. in painting) to work as a cashier (to speed the checkout during heavy traffic)?
  • What is the relationship between traffic and engagement? For example, if we put a coffee station in the high traffic aisle, will people stop?

B(Pro Tip) Occupancy is a versatile metric to test for “Local Demand”. It’s used in quick A/B Testing of core assumptions and designing layouts.

#4 Friction Points: Identify Abandon Behaviors & Prevent Friction in the Customer’s Experience

Abandons, bottlenecks, and checkouts create obstacles to the shopping process and the customer’s experience.

The most understood point of friction in the store is the Checkout.

With Queue Management, Kroger Supermarkets reduced their average waiting time from 4 minutes to 30 seconds and increased sales by 1-2%.

Amazon Go (Source Amazon)

And, of course, there is Amazon Go

Always migrate your audience to the path of least resistance. Kintan Brahmbhatt @ Amazon

Abandon Behaviors consist of:

  • Leaving Activity (i.e. people leaving the line to the checkout)
  • Not Starting Activity (i.e. not using counter service in lunchtime)
  • Not Entering the Store (i.e. not entering bank on the 1st of the Month)
  • Not Returning to Store (i.e. people really remember “bad service”)

(Pro Tip) Preventing friction is currently a much-discussed topic in retail innovation. But the solution is not technology. Many times retailers can avoid frictions just by paying attention to policies, procedures, and process.

Now that we identified the Path to Purchase and we quantified the TouchPoints, we track customers behaviors.

#5 Customer Engagement: Measure Optimal Stay Time & Capture the Customer’s Level of Interest

There are 2 dimensions in the analytics of Customer Engagement:

  • Tracking shopping behaviors within a virtual zone
  • Personal tracking of a customer across the store

The definition of Time-Based Metrics may vary, but the time a customer spends in a particular location indicates engagement.

Here are some case studies from people tracking solutions:

In Store Customer Journey -Engagement with Optimal Stay Time KPI | Behavior Analytics Academy
Customer Engagement (Behavior Analytics Academy)
  • Engagement Threshold: In an apparel store, the engagement threshold for black pants [on a flat display] was over 20 seconds. For premium-priced shirts, the threshold was 40 seconds.
  • Optimal Engage Time: In a beauty store, the optimal time in front of a product display is between 15 seconds to 90 seconds. In the makeup counter, an optimal time is above 60 seconds.
  • Engage Time & Path Analysis: In a supermarket, customer behaviors changed per path. In other words, Engage Time depended on previous engagement behaviors.

(Pro Tip) Time-Based Metrics are ideal for quantifying the Buyer’s Journey & Customer Engagement. Optimal Stay Time (OTP) is defined for each product, and it depends on customer behaviors.

#6 Service Time: Measure Customer Service & Improve Service Quality and Staff Productivity

What’s the value of a “great” salesperson?

If you ever walked into a store with the intent to spend $20 and left with sales of over $1000… you know what I’m talking about…

Telecom companies, for example, have an Optimal Service Time to sell their phone and internet plans. And they design their concept stores accordingly.

Service is a critical component in the success of physical stores. Just ask Best Buy.

Where to “measure” Service Time?

  • Structured Customer Service, such as counter service in a deli
  • Roaming Sales Associates, such as selling jewelry or electronics
  • Ad-Hoc Encounters, which are typically found in no-touch dept stores.

(Pro Tip) A variety of technologies provide insights into Service Time. Together with Service Intensity and Service Productivity KPIs, Service Time plays an important role in analyzing the power of sales associates.

#7 Product Positioning: Clarify the Calls to Action in Product’s Touchpoint & Increase Conversions

What can retailers do today & quickly with tracking people in the store?

Quick Wins come from Product Positioning!

Online CRO (Conversion Rate Optimization) dedicates much to the optimization of Calls to Action (CTA).

In physical stores, there are no red buttons that customers can click.

Instead of linear concepts of step-by-step frameworks, in-store customer journey mapping relies on real-time data on planograms and pricing.

Product Positioning (Behavior Analytics Academy)

Actionable insights on Product Positioning:

  • Clearance Racks: Many Merchandising Mavens think that stores follow their guidelines. Tracking customer behaviors around clearance racks are eye-opening to retail executives who care about profits.
  • Staff Expertise: In certain products – such as jewelry, electronics, and even construction – the availability of an expert associate makes a huge difference in sales
  • End Cap Positioning: Brands tend to pay extra for positioning their products in high-traffic end caps. Here’s a hard truth – high volume of traffic does not necessarily mean customer engagement.

(Pro Tip) Actionable insights from the optimization on Product, Promotion, Price, and Place can generate a spike in purchases.

It’s not enough to know what people needs and desires are. We also need to know how that interacts with the store’s environment – Ted Burks (YouTube)

Advantages of Designing the Customer Journey with “In-Store Dynamic Maps”

In Behavior Analytics, we merge and adapt Behavior Science, Online CRO, and Location Analytics to optimize the retail store for conversions and profit.

Start today.

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Ronny Max

Ronny Max is the founder & principal of Behavior Analytics Academy and Silicon Waves Agency. Ronny worked with 250 and more retailers, brands, agencies, solution providers, technology startups, market research, venture capital, and research institutions on people tracking technologies, customer's journey projects, and in-store optimization. Silicon Waves provides advisory, training, and consulting to C-Level Executives, Investment Firms, Product Management, Customer Success & Direct-to-Consumer Teams. In 2015, Ronny served as the retail expert at Stanford University Vision Project. In 2017, the Behavior Analytics Academy is born. The Academy provides on-demand online courses and live workshops on optimizing conversion rates and profits in physical retail.