How to choose the right in-store analytics for your business

December 11, 2017
Here is a conversation about in-store analytics that we’ve had far too often with managers and franchise owners:

Manager: What does Solink do?

Us: We create a central video and data analytics platform for multi-unit businesses.

Manager: So it’s like video analytics? I have that in my stores.

Us: What do you use the analytics for?

Manager: Well… nothing really. I just have a bunch of stats.

Does this sound familiar to you? As believers that data can improve all areas of a business, it pains us to see someone who is paying for in-store analytics but not actually using them to get a return on their investment. We want to change this conversation.

Data Analytics is not about measuring ALL the things. It’s about measuring the RIGHT things.

Today there are so many different types of applications for in-store analytics that choosing the right ones for your business can be a challenge. Not to mention that some of the applications on the market are made for data scientists, not managers or fraud investigators.

The right in-store analytics will help you make decisions and enact changes in your business where it matters most. This may sound obvious but it’s easy to forget.

For instance, we provide risk analysis to short term money lenders. While we can offer them applications for customer service and marketing, what they really need is a better way to audit loans for potential fraud. By solving that one problem, they see a significant ROI from using our fraud applications.

Having a well-thought out data analytics strategy will help you to avoid costly mistakes and focuses your efforts on the areas of the business that will benefit the most from analytics. While the strategy will be vastly different for each company, we put together these 4 questions which are critical to answer before making an investment in data analytics.

4 Questions To Help You Formulate A Data Analytics Strategy

These questions cover Context, Need, Data and Risk. For simplicity, we assumed that you already have a pretty clear idea of the problem you are trying to solve, be it internal fraud, slow service times, low customer satisfaction or simply looking to increase revenues.

1. What Is Your Context?

In previous blog posts, we stressed the importance of context in data analytics. You can read more about this topic here, but essentially what you need to know is that without context (or in the wrong context), your information might be inaccurate, misleading or simply useless.

Start by defining the context of the problem(s) you are trying to solve. For instance, if you want to make sure that customers are not waiting at the drive-thru window for longer than 30 seconds to pick up their order, you need an analytics tool that not only tracks how long each customer is waiting but also notifies you of slow service in near real-time. This way you react to situations as they happens. In a different context, you may be looking at long-term planning and only need to look at the drive-thru performance metrics at the end of the week, month or quarter.

To determine the context, ask yourself the following questions:

  • who needs to look at the analytics?
  • how often and when is the information needed?
  • how much information do I need? (very detailed vs a brief overview)
  • will this help in day-to-day operations, long-term decisions or both?
  • how much time can I devote to this?

2. Do you need a Vitamin or a Painkiller?

Vitamins are great when we want to boost performance and ensure long-term health. But painkillers solve an urgent and often unbearable problem.

Analytic applications work in much the same way so you need to determine which type of application will have the biggest impact on your business.

The most important thing to remember is that you don’t want to get distracted by ‘nice to have’ applications (aka vitamins), when there are more pressing issues on hand. Generally speaking, the applications that solve a frustrating and urgent pain have a greater, more clear-cut ROI, at least in the short-term.

Here are some guidelines to help you determine your situation and the type of in-store analytics that are most appropriate:

Vitamins Painkillers
Phrases “nice to have”
“this might be an opportunity”
“how can we be even better?”
“must have”
“we need to fix this problem”
“the current solution is frustrating”
What Drives Adoptions Wanting to be more innovative
Looking to add new technologies
Improving the customer experience
Finding ways to be more efficient
Getting a competitive advantage
Change in legislation
Customer demands
New security requirements
Increase in fraud cases
Need to cut down on losses
Can’t keep up with auditing
Company is losing money/employee time
Example Using heat mapping to determine how customers move through the store Using Exception Based Reporting to detect internal fraud

3. What type of data should be analyzed?

There’s another way that context is relevant to your data analytics. By looking at the same transaction or in-store event from the point of view of multiple data sources, you get a more complete image of the situation.

For instance, if you want to detect internal fraud at the POS system, using POS Exception Reporting will only give you part of the story. You also need to integrate the video data from security cameras into the analytics platform. This is how we created Contextual Analytics.

In fact, all of our applications fall under the category of Contextual Analytics because we always look at a situation or problem from multiple data sources and we highly suggest you do the same.

That being said, determine which data sources are relevant in answering your business challenge.

Here are some of the most important data sources which, when combined, make the analytics much more valuable:

  • POS systems
  • Security cameras
  • ATM machines
  • Sensors
  • Access Control
  • Employee discount cards

4. What is the risk?

At this point, you have already narrowed down to the type of applications you need. The last step is to consider the requirements or risks associated with them and what you are willing to put up with. As a manager, executive or business owner, this step will be very familiar to you.

The important risk elements to consider:Direct Financial investment: installation costs, recurring payments, cost to updateTime investment: how long will it take to learn to use the application, how much time it will require daily/weeklyChanges that need to be made: will you have to update existing hardware such as cameras, hire additional employees to run the applications


Answering the four major questions from this article will provide you with a good start to putting together a data analytics strategy. If you are already using some applications, it will help you to evaluate whether those are appropriate for your current business needs or not. It’s important to keep in mind that no matter what type of in-store analytics you are using, they should always have a direct link to KPIs such as service speed or employee theft. This will ensure that you are getting valuable information from your analytics, not just reports and statistics.

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