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3 Ways excess data can stunt your growth

Excess Data And Growth

We love data. But we also know excess data can be a bad thing. Here are three ways we’ve seen retailers use data to their detriment and some ideas on how to avoid these pitfalls.

1. Analyzing anecdotes

One data challenge we’ve seen is people manually reviewing too much data anecdotally.

Examining sales volumes for the last time you ran a specific promotion is valuable. However, a single person can only consider a few data points at a time, and that data is often open to interpretation (which is often biased).

At one retailer, team members in three functional areas, were each reviewing historical anecdotes, estimating the impact of the same up-coming promotions, and arriving at different conclusions.

Analyzing anecdotes uses valuable time, and bias can lead you in the wrong direction.

Strong analytics will curb reliance on anecdotes.

Teams will always want to see the sales history. By incorporating strong analytics, you bring a lot of value! To deliver even more value, we suggest providing demand insights based on proven modeling techniques. These will steer teams to better informed, less biased decisions. This investment in data analysis, paired with an explanation of insights will also curb reliance on so much historical data.

2. Getting lost in the large data

Want to know if the recipe discount you send to Jasmine’s mobile account will inspire her to buy your lemon-infused Tunisian olive oil at your Kansas City store the second week of July? To get even more specific, perhaps you also want to know if she will purchase it the second week of July, despite the high chance of rain.

While many systems can mechanically model customer demand at very specific levels of data, the nearly unlimited factors result in unreliable excess data.

Systems with erratic recommendations are burdensome to manage, resulting in people not using them. By not utilizing these systems, however, there is less effective decision-making.

A balanced approach will help ensure rich and reliable results.

Well-equipped implementers, data analysts, and solution providers can balance detailed insights and dependable results in a variety of ways.

However, when one retailer reached out for help with their forecast challenges, they painfully admitted – “Our last implementor was very good – they did everything exactly as we asked them.”

Pushing for very specific approaches to demand modeling may pressure your implementor or data teams to make you happy in the short term. Instead, being clear about where you are looking for value, how you will leverage the forecast, and your bandwidth to manage exceptions will help produce detail-rich forecasts in which you and your teams can have long-term confidence.

3. Bigger is better syndrome

With so much data available to retailers today, some can become eager to leverage more of it than is necessary to address today’s most pressing opportunities.

However, that excess data can be difficult and time-consuming to access. Data-intensive analysis can leave other important, more easily addressed challenges unattended, making valuable insights left unmined and unseen.

The right data for the job will help give you higher returns on your effort. Petabytes of data and shiny new tools can be enchanting – and in many cases, are worthwhile. But the most powerful and valuable approaches to leveraging data in retail are not all new. Being open to a variety of data sources and solutions can help you get the most value for your investment.

Can you think of other ways too much data can stop progress or bring harm to your business?

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About Cognira

About Cognira

Cognira is the leading artificial intelligence solutions provider for retailers. Cognira is passionate about helping retailers unlock valuable, transformative business insights from their data.

We know retail. We love data.

To learn more, check out our website at cognira.com or contact us today to get started. 

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