Retail Analytics: 101
Retail Analytics- What is it?
Retail analytics is essentially making use of data to augment decision-making. The outcomes of these decisions are measured through metrics and KPIs that serve as proxies for the health of the business.
Data can come from many sources...
- Daily transactions
- Day-to-day Operations
- Shopper Loyalty Initiatives
- Product Information
- Marketing Attribution Tools
- External data sets (e.g. Weather)
New technologies have allowed us to augment our analytical prowess through the combination of data sets (e.g. promotions effectiveness derived from Marketing Attribution and Sales Data) to paint a more complete picture before making a decision.
With the proliferation of sensory (e.g. IoT devices) and the reduction in cost for storage and compute in cloud technologies, it is now easier than ever to collect, store and analyse data.
The goal post, however, hasn't changed.
We don't want to stare that visuals and charts, we want actionable insights behind the analytics.
i.e. What should be my next action to help me increase sales or decrease cost?
Many larger organizations have had a head start in this data revolution. With deeper pockets and well-crafted data strategies, many have already made significant technological investments to cement their lead.
Does this mean that all is lost for the little guys?
Starting out small with a clear strategic focus on how to leverage data early can give new entrants a huge leg up the competition. Larger enterprises have two major challenges ahead of them.
- Internal Adoption
Adoption of technology means nothing if the right stakeholders haven't learnt how to leverage analytics and adapt decisions based on the data. Unfortunately, no single piece of enterprise technology is enough to drive a culture shift towards decision making.
Onto this guide…
About this guide
Who is this for…
This guide is focused on the little guy, the small retail businesses who might be thinking through an analytics implementation but have no idea where to start.
This is meant to be a crash course for those interested in learning more about retail analytics and how you might go about using some tools to help you.
You should not continue reading if you…
- started out only 6 weeks ago
- have <100 monthly transactions (e-commerce)
- are NOT committed to improving the current situation
This guide will not…
- Go through highly technical formulas
- Recommend/ market any specific tool
Why we wrote this..
Haste is a productivity solution that abstracts away the complexities that come with data for retailers.
We offer relevant insights to the right teams, at the right time, so that they can make the most informed decisions.
We believe that retailers should only have to focus on three core areas-
- Developing their Employees,
- Bettering their Products & Services and;
- Serving their Customers.
Today, knowledge and time is arbitrage, and Our Mission is to power productivity the in world of commerce.
To us, this means providing the tools so that retailers can be at their best... all the damn time.
The bulk of our work is done with SMB retailers and brands, and to prepare them for the next chapter in their growth, we wrote this guide.
Why are you investing in an analytics tool?
Note: This section will cover the high-level concepts, skip ahead for the practical concepts.
You don’t need an analytics tool. You need a job to be done.
If you are looking for a magical pill to 10x your sales in the next 90 minutes, you are probably looking in the wrong place.
You need a solution to your current problem.
Analytics is a tool that is meant to help, but if you don’t have the problem statement figured out, staring at dashboards and tracking KPIs and metrics isn’t going to help.
Think about this as a doctor visit…
- You are healthy but you want to keep fit
- You are sick as hell and you need medicine
In the former, your business is doing pretty alright, but you want to know if you could be doing any better.
For this visit, you want a checkup to benchmark yourself against peers and see if there are optimizations you could do to improve/ maintain the current state.
In the latter, your business is dying slowly, and you see the symptoms; terrible profits and every metric in on the way down.
Chances are, because you never invested in the health of your business, you have no idea what’s going on. For this visit, you just want to take some quick medication (e.g. double online marketing spend) to get better quickly.
Moving away from the analogies…
Your need should fall into two buckets…
1. Monitor information
Assuming you are operating in an omni-channel environment, you should be trying to collect data from both online and offline channels to bring together a holistic dashboard that can be used to organize and monitor information about everything that is going on.
Some goals here include monitoring...
- Inventory sell-through rates,
- Promotional performance
- Basket compositions
- Pricing effects.
2. Optimize opportunities
With the basic infrastructure ready, you are probably thinking about how to start turning $1 into $3.
- Funnel analysis to improve the number of completed transactions
- Improve social media ad set targeting to decrease acquisition cost
- Promotional mechanics to improve average order value
Which level of retail analytics is suitable for you?
This section is going to be a brief description of what are the different levels of retail analytics and where you should be focusing your attention on.
Level of Analytics in a layman’s definition
- Descriptive: What has happened?
- Diagnostic: Why did it happen?
- Predictive: What is going to happen?
- Prescriptive: What should you do?
Descriptive Analytics – What has happened?
This is a reporting function that conveys historical data in a digestible format. In the retail context…
- How did your sales perform in the last quarter?
- Which products or categories didn’t hit their target?
Notice that at this level, some level of benchmarking will be required for you to understand whether what has happened is actually good or bad.
You could either do this against historical data, bottom-up forecasted estimates, external data sources.
Pro Tip: You need to set internal targets
If the numbers showed you that you were growing your business 10% month over month, is that a good thing? If there was a slow down in one store but an increase in sales on your online affiliate channels, is that good news?
This is the most basic form of analytics and you shouldn’t move forward without it
Diagnostic Analytics – Why did it happen?
On some level, this is like detective work. Figuring out what happened based on what was out of place.
Say you discounted a new merchandise for 3 months and sales skyrocket.
You try the same trick for the another 3 months. It didn't work out so well.
A spike in sales can be attributed to a…
- promotion you ran
- new product release
- seasonal holiday
This may seem like a tedious process at first but once you learn the reason, you become capable of replicating a success (or avoiding a mistake).
Pro Tip: You might not be able to come to a definitive conclusion on only one test. Record your findings to revisit them later. Be SUPER specific about experiment parameters.
If you don’t have a tool that allows you to experiment, use a company Google Sheet or a group chat to record all these findings so that someone can pick up where you left off.
If you have the suspicion that the X’mas break was the cause of a surge in sales, you can only test the same hypothesis next year so remember to record something like this-
- Fashion line sales expected to increase by 450%, 2 weeks before X’mas possibly due to the promotional campaign launched during the last week of November.
When the holidays come around next year, you can try to run some advertisements 3 weeks before X’mas to see if sales really to spike up by that much.
Predictive Analytics – What is going to happen?
The most common use case for predictive analytics is forecasting sales volume.
This could impact you in numerous ways including expected sell-through rates, manpower required to fulfill demand and the optimal level of safety stock.
It can be so much more. Consider bottom-up marketing estimates-
You know that last month,
…spending $2,000 on Facebook ads…
…got you 10,000 referral visits…
…and 1% of them converted…
…at an average transaction value of $50
Next month, what could you achieve if you…
… 2x Facebook ad budget
…improved conversion rates to 1.2%
Estimated Sales Impact: 166% increase (after marketing cost)before cost of goods sold
Predictive analytics can be a powerful tool in a retail setting if you are able to isolate the variables to replicate the effect on sales.
Pro Tip: Variables don’t correlate linearly, you have to isolate the range in which your estimates hold true and test again in the future.
Prescriptive Analytics – What should you do?
Prescriptive analytics pieces together learning from the other levels to offer recommendations as to what to do next.
The most common example: Google Maps
This is the holy grail of data science but incredibly difficult in practice- shoppers are rarely rational, and variables change all the time.
However, with enough data, you could get quite close. Let’s say you had enough data…
- Rainy days increase the likelihood of impulse purchases by X%, you could stock more merchandise closer to the point of purchase
- Bright lights made shoppers more likely to purchase snacks, you could move the snack section to a more well-lit area of the store
- Showing a video ad with 67sec exactly has the highest conversion rates you could simply triple video product budget to fit this
Although prescriptive analytics has the most direct impact on business alignment, the experimentation infrastructure required has many retailers thinking twice about investing in such technologies.
Pro Tip: As for 2019, this technology is drawing closer. Start investing in the infrastructure early.
The previous section gave an excellent overview as to how you might evaluate the use cases of retail analytics, this section focuses on the hard truth- the organization might not be ready.
You might already be sold on the promises of retail analytics and have all the problems mapped out. Do you have the answers to the next two questions?
- What problems do I want to solve? (done)
- What sort datasets do I have on hand?
- What is the general level of expertise of my team?
Before jumping the gun and researching the best analytics solutions for you, take a quick look at what you have that is helping you store and manage data.
Even if you only have access to technology that may be a decade old, you should have a point-of-sale system that logs your sales transactions and merchandise information.
If you are an early adopter, you should have some of these in place
- Customer Relationship Management Software: Shopper
- In-store Sensors (Offline): In-store Traffic and Behavior
- Inventory Management System: Product
- Google Analytics (Online): Website Traffic and Behavior
Each one gives you a unique dataset which you can use for analytics. We will run through some examples in a later section on how you can apply some of these to optimize your store- but there are two important points to highlight before moving forward. You have to ensure that the data is accessible and clean
Accessibility to Data
Regardless of the software vendor, you should have the ability to extract the raw files for your own work.
Take the point of sales system as an example, the data stored there might be required for accounting, inventory management and analytics.
Word of caution: Legacy point-of-sales vendors out there might not allow you access to your own data.
In today’s cloud-enabled market, most software providers have and integration option or an open API that you can leverage to seamlessly integrate with the multiple tools that you will want to use.
If you are have already committed to a vendor and they are not all that you thought they would be, change. Now. Before it is too late. There are lots of cloud-based solutions that don’t cost more than $100 a month.
Cleanliness of Data
We have all heard phrases like “Data is the new oil”.
The truth is, oil is pretty useless in its' unrefined state.
An analytics platform is like a refinery that helps you make use of it. A refinery isn’t going to work as well if your data set isn’t as well maintained.
If the dates are all messed up or the products are mislabeled, the data is useless- clean it or trash it (depending on the state).
Word of Caution: Older third-rate vendors might not have kept up with technology best practices. Not all have proper database structures.
It commonly happens because they didn’t come from your specific sector of retail and thus might not understand the complexities behind it.
Take for example if they have been catering to bookstores. If they were to sell the same system to a meat grocer, they would suddenly have to capture data like the price per weight which isn’t something that their database was engineered for.
This will likely mean some shoddy patchwork to get a system up and running which can lead to complications down the line. Run.
Now that you have the data set prepared and the raw data ready, it’s time to take a good hard look at the team.
Good talent is hard to come by.
You might have an amazing team that you love to work with but we all have to face the reality that individuals who possess strong analytical skills are in hot demand and hard to recruit and keep.
So, how committed are you?
Scenario 1: No one is fully committed
If this scenario sounds like your business right now, you are not alone but you should start questioning the sustainability.
You might be allocating half a day a week to this but realistically, it’s a full-time job.
Think about how much time this might be costing you…
- Time spent on research
- Time spent on learning new formulas
- Time spent on repetitive task (e.g. supplier-level forecasting)
- Time spent on monitoring changes (e.g. effect of price changes)
- Time spent on ad-hoc task (e.g. calculating promotional effectiveness)
- Time spent searching for new insights
Get help. Hire an analyst or get tools that could help you automate at least some of the analysis.
Scenario 2: Two full-time analyst
You already have more resources than most. Having analytics be one person’s full-time job gives you a massive advantage over them.
Notes about management justification
Unlike the role of a social media marketer or an in-store salesperson, you might find it difficult to justify their presence in your company.
To make matter’s worse, analytics and big data is probably not your area of expertise.
If they are worth their salt, they should be able to justify their own presence.
If you bring a problem to them,
- Do they ask questions to clarify?
- Do they justify their use of a particular metric?
- Do they benchmark their results against those metrics?
- Can they run experiments on their own?
- Can they communicate their findings?
- Did they record their findings?
If they can do more than 70% of those things, you have a great team.
Notes about enablement
Some small business owners are reluctant to purchase tools that could help their analytics team work faster. This is an expensive mistake that you should not make.
You might have a…
- Marketing automation tool for your marketing team
- Procurement system for your procurement team
- Payroll tool for your human resource team
Why are you holding on the team back?
The average business analyst salary is USD$75,000 per year.
Would you rather use software to enable them to work more efficiently or hire another two analysts to pick up the workload?
Scenario 3: Data engineers to Data scientist
Not much to say here. You are probably within the top 5%
If you are in this state,
- Buy all the tools you can get your hands on to enable this team, even if they cost half a million every year
- Stop reading this guide because you are probably already making over $250 million a year.
When would be the best time to invest in analytics?
Here is a rough guide…
- Stage 1: Get a BI Tool
- Stage 2: Basic Predictive Tools
- Stage 3: Advanced Descriptive Toll
- Stage 4: Advanced Prescriptive
*If you are a pure e-commerce company, the bar for monthly transactions is much lower because data is much more readily available
Stage 1: 1-3 Stores or <1k monthly transactions
You are probably so strapped for time that you can’t afford to be spending your whole day in front of spreadsheets.
Use some form of business intelligence tool to keep you updated about how the business is doing every day.
Stage 2: 3-10 Stores or <5k monthly transactions
This stage is about setting the infrastructure in place.
With business growing steadily every month, you are going to want to have the right people and tools in place.
Setting KPIs and metrics only make sense if everyone can be aligned to some business metric and every manager is working towards these goals
Stage 3: 11-25 Stores or <25k monthly transactions
There are way too many things for one manager to handle at this stage.
You are going to need to give them the right reports for them to make decisions else profitability is going to be heavily compromised.
If you are still the central point of contact for decisions, you should be very worried.
Set up alert systems and processes in place for contingencies when things don’t go according to plan.
Use tools that help you set alerts and plan for the future to get the work done beforehand.
Stage 4: >25 Stores or >25k monthly transactions
If you don’t have all the analytics tools in place, it might already be too late.
At this juncture, analytics is no longer a “good-to-have”. The benefits of optimization compounds.
If at 20,000 transactions they find a way to save $1 or get an extra $1 per transaction, that’s $20,000 in profits. If they can repeat that process and your business grows 5% month-on-month, that’s approximately $300,000 added to the bank account in twelve months.
Domains & Sub Domains
Before we jump into the section about KPIs, Metrics and Formulas, we should list out all the relevant domains and sub-domains that are within your control.
Store: Location, Layout
Product: Sell-through rates, Safety Stock, Economic Order Quantity, Packaging
Price: Pricing Effectiveness, Effect on Complements & Substitutes, Effect on Basket Composition
Promotions: Marginal Uplift, Promotional Mechanics, Target Audience, Effect on Basket Composition
Shoppers: Demographical and Behavioral Segmentation, Price Sensitivity, Promotional Effectiveness, Brand Loyalty
Marketing: Channel Performance, Ad Format, Copywriting, Engagement Value, Funnel Performance
Retail KPIs, Metrics & Formulas
1.Sales Revenue & Gross Profits
Every other metric should be mapped to either sales revenues or gross profits (or both).
2. Average Margin
Layman’s Definition: Amount left after subtracting product cost
The average margin provides an indicator of how healthy the “safety cash” is in your business. This is important for two reasons:
- When marketing starts working, you will want to invest more of this extra cash up front to increase the effectiveness
- When a sales spike is anticipated, you want to be able to have extra inventory on-hand.
Layman’s Definition: Number of visitors entering your store (physical or online)
4. Number of Completed Transactions
Layman’s Definition: Number of completed transactions (duh)
If you have the resources, you should store data on the number of overall completed transaction and the number of transactions after returns.
5. Bonus: Number of Returns
Layman’s Definition: Number of product disappointments
6. Average Transaction Value
Layman’s Definition: Average amount spent by each customer per transaction
7. Number of Products per Transaction
Layman’s Definition: Average number of products bought by each customer per transaction
8. Conversion Rates
Layman’s Definition: Percentage of visitors that took the next step in the funnel
9. Average Promotional Uplift
Layman’s Definition: Average incremental benefit gained from a group of promotions
10. Customer Acquisition Cost
Layman’s Definition: Incremental cost of acquiring a new customer
11. Customer Lifetime Value
Layman’s Definition: Profits that a customer may spend with your business over her/his lifetime
12. Basket Analysis
Layman’s Definition: Composition of merchandise based on what was bought by shoppers
Success in the Long Term
Cohorts are the definitive indicator of the longevity of your business.
Some venture capitalist who even go so far as to term it as the “atomic unit of measurement”.
A cohort, in Jan 2020, will consist of
1. New Shoppers
2. Old Shoppers
In this segment of “Old Shoppers”, it can be broken down into:
– From Dec 2019
– From Nov 2019
– From Oct 2019
– From the first month
A cohort analysis will reveal the long-term health of your business and this historical analysis (adjusted for optimizations done over time) should reveal how well your stores will perform in the long-term and what would happen if you opened up more stores.
Thank you so much for taking the time to read this entire guide.
If you have questions or feedback, we’d love to hear from you in the comments.
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To measure is to know- Lord Kelvin