Today, there are so many technological options to collect, store and manage your data. It doesn’t matter that you are running a three-month-old store or a twenty-year-old franchise. You have no excuses anymore not to have a long-term data strategy.
With all these advances, your competition is getting smarter about using data to hedge their bets in the future.

Have you?

If you haven’t, start now before it is too late. Using data effectively requires time and experimentation. If you outlast or outsmart your competitors, you can still edge them out of the market. Those that started prematurely might not have fully formed a well-thought-out strategy, and that’s the arbitrage. Are you going to take advantage of it?

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Start with this thought framework

Don’t invest a single dollar in any analytics implementation until you can clearly answer these questions-

  1. What problems do I want to solve? (done)
  2. What sort datasets do I have on hand?
  3. What is the level of expertise of my team?

Always start with defining the problem and the optimal outcome. Here is an example-

  • We want to improve our cash flow.
    • Get a basic analytics tool for detective work
  •  There is an opportunity to reduce our cash locked in inventory by 40% by the end of the year
    • Get a more advanced tool to find out how
  •  Oh! If we improve our demand forecast models by 20%, we don’t have to carry that much inventory in our warehouse
  • Hey, look at that. More money in the bank that can be reinvested into other assets to grow the business.

Yes, it may seem like an optimistic outcome, but you will be surprised how often these little things happen.

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Some realism about your current infrastructure

Now that you have a problem statement scoped out, it doesn’t mean you have everything you need to grow. Look at the basic systems around you. The data has to be clean and accessible. Many merchants skimp over this part when they are setting up their stores because data just wasn’t a priority back then.
What if you used a Point of Sale vendor that didn’t allow you to extract the raw transaction data? Imagine the horror. Being locked into a third-rate platform that cannot integrate with your other tools.

A point-of-sale system (POS) contains the most fundamental sources of data and if you picked a lousy vendor, switch out now. There are great cloud-based alternatives other there that cost less than $100 a month.

If you don’t have a proper POS system in place, don’t bother reading on because you won’t be ready to succeed in the long run.

The next few things to get in place include-

  • Customer Relationship Management (CRM) Software: Shopper
  • In-store Sensors (Offline): In-store Traffic and Behaviour
  • Inventory Management System: Product
  • Google Analytics (Online): Website Traffic and Behaviour

Think of these tools as seeds and the data that they collect as fruit.

Side Note: Too Much Data

We won’t touch too much on this but if you have a significant amount of data being generated every month (millions of transaction or terabytes worth). It might be time to hire data engineers and build a data warehouse.
Think of it as building a room to house and store data. Data engineers help make the shelves and pack the boxes so that the data is stored in an orderly and easily accessible format.

 

Enabling the data team

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With the right the infrastructure set up and the data streaming in, it is time to start your analytics work.

There are dozens of techniques to solve the same problem, and you have to pick a solution that best suits you. Many make the mistake of falling into the technology hype and purchase a tool that doesn’t actually fit their specific workflow.

So how do you pick an analytics tool that your team will feel comfortable using?

Assuming you are not a subject matter expert, let them choose because they are more aware of

  1. Limitations of the data available
  2. Their own limitations

Before you had the tools in place, they were probably already using some other way to get the same outcome. Whether it was complex SQL queries or super pivot tables in Excel, they know how to work with the data they have.
They are less likely to recommend a tool that is too complicated for them to use and they are more suited to screen out retail analytics solutions that cannot work with the data set.

It is always tempting to think that you can get by forever without purchasing an analytics implementation tool because your team can always find a better way out. Here are some facts;

  • The data scientist or business analyst on your staff should probably be the most well paid
  • They are likely to be underappreciated because many managers don’t know how to set goals and objectives for them
  • They will leave soon because their skills are in hot demand and the tools that you are offering them are taking too much of their time.
  • We are not saying that you HAVE to purchase an analytics tool for them. You are going to need some financial justifications.

Now let’s think about it this way-

  • You pay them $50,000 a year (on the lowest bound)
  • The analytics tools reduces the average workload and time taken by a factor of only 2
  • You just reduced their workload to such a degree that they now…
    • Can finally spend their time looking for other ways to improve your business
    • Complete 10 more task in the same time frame
    • Love you

You just made your analytics operations 2x more efficient for the cost of software. How does the software compare? Does it cost the same as hiring another analyst?

Depending on the stage of your business, these assumptions might or might not hold true. If you haven’t even got the basic tools, getting one now might increase the efficiency by a factor of 10x.

Building a long-term Data Strategy

In essence, this strategy comes down to establishing the infrastructure required for analytics teams to move fast and execute. These teams need three things-

  • Access
  • Commitment
  • Enablement
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Access-

Depending on the contents of the raw data, some departments might be uncomfortable disclosing the numbers to the analysis. Say, a marketing department wildly overspends on an unsuccessful promotional campaign. It might be embarrassing for them to share these numbers with others in the organisation.
Your duty as someone part of this organisation (even if you don’t have a leadership title) is to encourage this sharing of data.

Commitment-

Like it or not, the success of this strategy hinges on the involvement of the senior management. It doesn’t matter how much you invest in your analytics team if their work is not going to be recognised by another department.
They are going to get jaded and just work on improving some other part of the organisation while that portion remains as it is.

Enablement-

Individuals who possess strong analytical skills are in hot demand. They are generally hard to recruit and retain. Don’t make them work with old tools that take forever to load.
Try letting them choose the tools that they prefer working with because they will be more aware of the limitations of the various tools and the data set available.

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Final Words

If you have any feedback or questions about this post, we would love to hear from you.

Good luck!

Posted by:Fu Fei

Data Geek, Passionate about enabling entrepreneurship Figuring out how to gamify retail operations Twitter: @SymbolOfFlight

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