Retail Business Intelligence: 7 Deadly Mistakes To Avoid

By

Aleks Tiupikov

Mar 10, 2024

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90% of retail stores operate on very low profit margins. Especially when it comes to high-velocity sectors like:

  • Grocery stores

  • Apparel

  • General Merchandise

On such high volume sales, each percent makes a lot of difference. The most successful companies in the retail industry are successful primarily because they know what works well for them and what doesn't. And they try to double-down on the good.

But how do you know what's good for your store and what's bad? When you have one small store and a limited number of products you sell - napkin math combined with your experience would probably give you the answer.

However, as your business starts expanding and you're opening up multiple locations, start working with more and more suppliers - this is when it's so easy to overlook this mindset and end up in a very low margin, dead loop.

A cycle where retailers find themselves trapped in a pattern of constantly seeking lower prices from suppliers to remain competitive, while simultaneously struggling to maintain profitability due to the resulting low margins.

That's where Business Intelligence comes into play.

In this article I'll walk you through the 7 mistakes I found many retail business owners make while trying to apply business intelligence tools and strategies to their retail companies.

For this article, I'll assume that you already have some BI software in place and a basic understanding of how to use it. If not, you can learn more about different BI tools and their applications here.

Mistake 1: Failing to identify and prioritize KPIs

When Alice approached the cat in Wonderland, she asked, "Where should I go?"

The cat replied, "That depends on where you are going."

Alice said, "I don't know."

"Then," said the cat, "it doesn't matter which way you go."

I really love this quote as it translates to so many things in life. And especially in business. It doesn't matter what kind of BI technology you have in place, if you don't know what your short term, mid term and long term goals are, you will never get the business intelligence working for you.

The biggest mistake that I noticed is however not just not having any KPIs in mind. But rather having the wrong business metrics that you might be trying to improve.

Once you've identified the KPIs, you need to review them on a monthly / weekly basis to understand if the direction you're going with new business ideas is bringing you back whatever you're trying to achieve.

Ideally you should have different KPIs for different areas of your business. Some basic metrics like gross margins, average transaction value and revenue growth should always be in front of you, but there are always specific metrics you should track related to the experiments you're running.

Here's a list of some good metrics that you should definitely consider starting with:

Sales Per Square Foot - a good way to measure a physical store's efficiency. Product Return Rate - The percentage of sold products that are returned by customers. Sell-Through - The percentage of units sold compared to the number of units that were available to be sold. Customer Satisfaction Rate - The percentage of customers who are satisfied with their experience and products. Average Basket Value - The average total amount spent per customer transaction.

Mistake 2: Neglecting to integrate data from multiple sources

Once you set up your BI infrastructure, one of the most important steps should be bringing all the data together in one spot in a way that can be easily analyzed using some software like PowerBI or Tableau.

It might be tempting to rely on some direct integrations with your ERP or POS software, but this approach often falls short. That's because the sources you bring together will be fragmented and give you an inconsistent view of your business.

By taking the time and investing in centralizing your data in a well-designed data warehouse, you open up a world of possibilities. You can combine data from various systems, such as your CRM, Point of Sale software combined with RFID tag data, financial software, etc. to gain a holistic understanding of what is going on in your business.

Now once you have everything in one place, you can easily dive into your KPIs and identify the low-hanging fruits or things that are affecting these KPIs. So you can freely ideate about the potential new things you can introduce to close these gaps.

This alone will set you apart from 80% of your competitors and seriously improve your margins long-term.

Mistake 3: Relying on outdated or inaccurate data

This one is so easy to overlook since it requires some technical know-how which you would typically find by hiring a consultant to do this every once in a while. You might also just have a dedicated team of data engineers that will maintain the quality of your data.

And even if you know for sure that the data you're seeing is fresh and updated, every time you create a new report there's a chance that you will do this incorrectly. This will result in pretty much wrong insights that can harm you a lot if you make some important decisions based on that.

The quick solution for that would be hiring a person who can do this analysis for you on request. It will cost money, and oftentimes take time for them to circle back with results. But at least you will be confident that the data is accurate.

A more sustainable approach in my opinion would be learning how to use BI software yourself (expect this to take some good time) or finding a more user-friendly solution that will still maintain highly accurate results.

A good rule of thumb here is to always double-check the data behind the most important decisions you undertake. Cause this is when the error-risk is at its highest.

Imagine this scenario: you're about to launch a major marketing campaign based on the actionable insights from your BI tool. The data suggests that a particular customer segment is ripe for targeting, and you allocate a substantial budget to reach them.

However, if the underlying data is flawed, you could be pouring resources into a campaign that's doomed to fail from the start.

Hope you get the point.

Mistake 4: Not leveraging predictive analytics

I think one of the main advantages of using retail business intelligence properly is the fact that the more data you collect, the more opportunities you will have to understand the potential future outcomes for different kinds of decisions you make.

Of course it requires some setup. There's no tool out there that will give you solid and reliable forecasting capabilities right out of the box. That's why so many companies skip this part. But I think the ones that don't are the most successful retail businesses out there.

Think Target, Amazon or Costco. All of them have whole departments in charge of estimating business outcomes for changes or market updates. They are really on top of it. That's one of the reasons why these companies are so successful.

And you don't need to have these huge teams to do that. Just think about the potential hire or outsource that can build a sophisticated model specific to your business. The returns would be huge.

These models can help you: anticipate future demand, optimize inventory levels and make more informed decisions about pricing, promotions and product assortments. That's the edge you will have that a lot of companies don't.

Moreover, you can use this data to optimize your store layouts, improve customer service, and even predict which products are likely to be returned. The list is never-ending. This is not easy, but the rewards are totally worth it.

Mistake 5: Failing to communicate insights effectively across the organization

Alright, now that we've covered the main operational mistakes companies make, I feel like the next big mistake lies in the internal company procedures related to business intelligence.

See, you can really nail the process of analyzing your data and gain so many insights into what you should or should not be focusing on. But at the end of the day, unless you're the key decision maker, you will have to onboard other people into your vision of a situation.

What I found actually happens in the real world is that there are people in lower positions like managers or just specialists who decide to dig into some issue and then discover an interesting trend or pattern.

And oftentimes instead of bringing this up they do nothing, thinking that it's either not very helpful/accurate (and they don't want to seem stupid) or nobody will listen to them.

This primarily happens for 2 reasons:

  1. The company has a top-down decision making structure where nobody in the middle can affect anything. This usually results in a very unproductive and stagnant environment.

  2. Upper management is promoting a "Don't bring me the problem without a solution" sort of thing which in reality makes people not share anything if they're unsure about the solution. Even though the best solutions are typically a collaborative effort.

So make sure you're not only investing in the software and talents around your business intelligence but also the underlying structure and culture for other people at your company to follow.

Mistake 6: Underestimating the importance of data security

Everyone I know in companies with more than 10 employees has told me that security of their customers' data is the #1 priority for them.

Yet half of them don't really understand what security means. And if their existing setup is really secure.

It's so tempting to just follow the marketing claims of different websites saying: "YOUR DATA IS ALWAYS SECURE" without doing any due diligence to understand if that's really the case.

And the reasons are obvious:

It takes money It takes expertise It takes time

It's just so much easier to simply trust whatever the website says and then blame them if anything happens.

And yes, maybe legally you can have some chances of defending yourself, but leaked data is leaked data.

Your customers will not think of you as an innocent bunch of people that were bitterly deceived by a malicious software provider.

And they'd be right. It's not their problem. They didn't trust some random website or salesperson. They trusted you - so you should take responsibility for that.

Investing in robust data security measures should be your top priority. This includes things like encryption, access controls, regular security audits, and employee training on data handling best practices.

Yes, it requires an upfront investment of time and resources. But the peace of mind and protection it provides is invaluable.

Don't be one of those companies that learns the hard way. Make data security a core part of your BI strategy from day one.

Mistake 7: Not continuously monitoring and adapting to changes in the market

The final mistake I commonly see is treating business intelligence as a one-and-done initiative.

Companies will invest in setting up a BI system, generate some initial insights, but then fail to iterate and improve over time. They fall into a "set it and forget it" mentality.

But the reality is, both your business and the wider retail landscape are constantly evolving.

Consumer preferences shift, new competitors emerge, economic conditions fluctuate. What worked well last quarter may not be optimal for your business now.

That's why I see the best retail companies continually monitor their KPIs, test new hypotheses, and refine their strategies based on the latest data.

It's an iterative cycle of analysis, action, and adjustment. You learn something new - you apply it and see if it works, and if it didn't, that means you learnt something new again!

So make sure to view BI as a living, breathing part of your business - not a static tool. This will position you for long-term success in this dynamic industry.

Final thoughts

By utilizing the valuable insights from customer experience data, retailers can make informed decisions to optimize marketing campaigns, improve customer service, and enhance the overall shopping experience.

Business intelligence solutions allow retailers to gain a deep understanding of customer behavior, preferences, and interactions throughout the customer journey.

This makes marketing teams create targeted campaigns and personalized product recommendations that drive customer engagement, satisfaction, and loyalty.

Moreover, retail business intelligence software plays a vital role in streamlining retail operations, including inventory management, supply chain operations, and product placements.

By analyzing real-time data on inventory levels, stock shortages, and sales transactions, you can optimize the merchandise offerings, reduce inventory costs, and improve operational efficiency.

Predictive analytics further enables to participate in consumer demand, forecast sales, and make data-driven decisions to stay ahead of market trends and seasonal fluctuations.

BI allows companies to create a data-driven culture and continuously adapt to changing consumer behavior and future trends.

By investing in advanced analytics solutions and fostering a culture of informed decision-making, you can unlock so many insights from your data you could never even think of.

This will allow you to deliver personalized experiences across various channels, including brick-and-mortar stores and mobile apps. Which will ultimately drive customer loyalty and long-term business success in an increasingly competitive landscape.

90% of retail stores operate on very low profit margins. Especially when it comes to high-velocity sectors like:

  • Grocery stores

  • Apparel

  • General Merchandise

On such high volume sales, each percent makes a lot of difference. The most successful companies in the retail industry are successful primarily because they know what works well for them and what doesn't. And they try to double-down on the good.

But how do you know what's good for your store and what's bad? When you have one small store and a limited number of products you sell - napkin math combined with your experience would probably give you the answer.

However, as your business starts expanding and you're opening up multiple locations, start working with more and more suppliers - this is when it's so easy to overlook this mindset and end up in a very low margin, dead loop.

A cycle where retailers find themselves trapped in a pattern of constantly seeking lower prices from suppliers to remain competitive, while simultaneously struggling to maintain profitability due to the resulting low margins.

That's where Business Intelligence comes into play.

In this article I'll walk you through the 7 mistakes I found many retail business owners make while trying to apply business intelligence tools and strategies to their retail companies.

For this article, I'll assume that you already have some BI software in place and a basic understanding of how to use it. If not, you can learn more about different BI tools and their applications here.

Mistake 1: Failing to identify and prioritize KPIs

When Alice approached the cat in Wonderland, she asked, "Where should I go?"

The cat replied, "That depends on where you are going."

Alice said, "I don't know."

"Then," said the cat, "it doesn't matter which way you go."

I really love this quote as it translates to so many things in life. And especially in business. It doesn't matter what kind of BI technology you have in place, if you don't know what your short term, mid term and long term goals are, you will never get the business intelligence working for you.

The biggest mistake that I noticed is however not just not having any KPIs in mind. But rather having the wrong business metrics that you might be trying to improve.

Once you've identified the KPIs, you need to review them on a monthly / weekly basis to understand if the direction you're going with new business ideas is bringing you back whatever you're trying to achieve.

Ideally you should have different KPIs for different areas of your business. Some basic metrics like gross margins, average transaction value and revenue growth should always be in front of you, but there are always specific metrics you should track related to the experiments you're running.

Here's a list of some good metrics that you should definitely consider starting with:

Sales Per Square Foot - a good way to measure a physical store's efficiency. Product Return Rate - The percentage of sold products that are returned by customers. Sell-Through - The percentage of units sold compared to the number of units that were available to be sold. Customer Satisfaction Rate - The percentage of customers who are satisfied with their experience and products. Average Basket Value - The average total amount spent per customer transaction.

Mistake 2: Neglecting to integrate data from multiple sources

Once you set up your BI infrastructure, one of the most important steps should be bringing all the data together in one spot in a way that can be easily analyzed using some software like PowerBI or Tableau.

It might be tempting to rely on some direct integrations with your ERP or POS software, but this approach often falls short. That's because the sources you bring together will be fragmented and give you an inconsistent view of your business.

By taking the time and investing in centralizing your data in a well-designed data warehouse, you open up a world of possibilities. You can combine data from various systems, such as your CRM, Point of Sale software combined with RFID tag data, financial software, etc. to gain a holistic understanding of what is going on in your business.

Now once you have everything in one place, you can easily dive into your KPIs and identify the low-hanging fruits or things that are affecting these KPIs. So you can freely ideate about the potential new things you can introduce to close these gaps.

This alone will set you apart from 80% of your competitors and seriously improve your margins long-term.

Mistake 3: Relying on outdated or inaccurate data

This one is so easy to overlook since it requires some technical know-how which you would typically find by hiring a consultant to do this every once in a while. You might also just have a dedicated team of data engineers that will maintain the quality of your data.

And even if you know for sure that the data you're seeing is fresh and updated, every time you create a new report there's a chance that you will do this incorrectly. This will result in pretty much wrong insights that can harm you a lot if you make some important decisions based on that.

The quick solution for that would be hiring a person who can do this analysis for you on request. It will cost money, and oftentimes take time for them to circle back with results. But at least you will be confident that the data is accurate.

A more sustainable approach in my opinion would be learning how to use BI software yourself (expect this to take some good time) or finding a more user-friendly solution that will still maintain highly accurate results.

A good rule of thumb here is to always double-check the data behind the most important decisions you undertake. Cause this is when the error-risk is at its highest.

Imagine this scenario: you're about to launch a major marketing campaign based on the actionable insights from your BI tool. The data suggests that a particular customer segment is ripe for targeting, and you allocate a substantial budget to reach them.

However, if the underlying data is flawed, you could be pouring resources into a campaign that's doomed to fail from the start.

Hope you get the point.

Mistake 4: Not leveraging predictive analytics

I think one of the main advantages of using retail business intelligence properly is the fact that the more data you collect, the more opportunities you will have to understand the potential future outcomes for different kinds of decisions you make.

Of course it requires some setup. There's no tool out there that will give you solid and reliable forecasting capabilities right out of the box. That's why so many companies skip this part. But I think the ones that don't are the most successful retail businesses out there.

Think Target, Amazon or Costco. All of them have whole departments in charge of estimating business outcomes for changes or market updates. They are really on top of it. That's one of the reasons why these companies are so successful.

And you don't need to have these huge teams to do that. Just think about the potential hire or outsource that can build a sophisticated model specific to your business. The returns would be huge.

These models can help you: anticipate future demand, optimize inventory levels and make more informed decisions about pricing, promotions and product assortments. That's the edge you will have that a lot of companies don't.

Moreover, you can use this data to optimize your store layouts, improve customer service, and even predict which products are likely to be returned. The list is never-ending. This is not easy, but the rewards are totally worth it.

Mistake 5: Failing to communicate insights effectively across the organization

Alright, now that we've covered the main operational mistakes companies make, I feel like the next big mistake lies in the internal company procedures related to business intelligence.

See, you can really nail the process of analyzing your data and gain so many insights into what you should or should not be focusing on. But at the end of the day, unless you're the key decision maker, you will have to onboard other people into your vision of a situation.

What I found actually happens in the real world is that there are people in lower positions like managers or just specialists who decide to dig into some issue and then discover an interesting trend or pattern.

And oftentimes instead of bringing this up they do nothing, thinking that it's either not very helpful/accurate (and they don't want to seem stupid) or nobody will listen to them.

This primarily happens for 2 reasons:

  1. The company has a top-down decision making structure where nobody in the middle can affect anything. This usually results in a very unproductive and stagnant environment.

  2. Upper management is promoting a "Don't bring me the problem without a solution" sort of thing which in reality makes people not share anything if they're unsure about the solution. Even though the best solutions are typically a collaborative effort.

So make sure you're not only investing in the software and talents around your business intelligence but also the underlying structure and culture for other people at your company to follow.

Mistake 6: Underestimating the importance of data security

Everyone I know in companies with more than 10 employees has told me that security of their customers' data is the #1 priority for them.

Yet half of them don't really understand what security means. And if their existing setup is really secure.

It's so tempting to just follow the marketing claims of different websites saying: "YOUR DATA IS ALWAYS SECURE" without doing any due diligence to understand if that's really the case.

And the reasons are obvious:

It takes money It takes expertise It takes time

It's just so much easier to simply trust whatever the website says and then blame them if anything happens.

And yes, maybe legally you can have some chances of defending yourself, but leaked data is leaked data.

Your customers will not think of you as an innocent bunch of people that were bitterly deceived by a malicious software provider.

And they'd be right. It's not their problem. They didn't trust some random website or salesperson. They trusted you - so you should take responsibility for that.

Investing in robust data security measures should be your top priority. This includes things like encryption, access controls, regular security audits, and employee training on data handling best practices.

Yes, it requires an upfront investment of time and resources. But the peace of mind and protection it provides is invaluable.

Don't be one of those companies that learns the hard way. Make data security a core part of your BI strategy from day one.

Mistake 7: Not continuously monitoring and adapting to changes in the market

The final mistake I commonly see is treating business intelligence as a one-and-done initiative.

Companies will invest in setting up a BI system, generate some initial insights, but then fail to iterate and improve over time. They fall into a "set it and forget it" mentality.

But the reality is, both your business and the wider retail landscape are constantly evolving.

Consumer preferences shift, new competitors emerge, economic conditions fluctuate. What worked well last quarter may not be optimal for your business now.

That's why I see the best retail companies continually monitor their KPIs, test new hypotheses, and refine their strategies based on the latest data.

It's an iterative cycle of analysis, action, and adjustment. You learn something new - you apply it and see if it works, and if it didn't, that means you learnt something new again!

So make sure to view BI as a living, breathing part of your business - not a static tool. This will position you for long-term success in this dynamic industry.

Final thoughts

By utilizing the valuable insights from customer experience data, retailers can make informed decisions to optimize marketing campaigns, improve customer service, and enhance the overall shopping experience.

Business intelligence solutions allow retailers to gain a deep understanding of customer behavior, preferences, and interactions throughout the customer journey.

This makes marketing teams create targeted campaigns and personalized product recommendations that drive customer engagement, satisfaction, and loyalty.

Moreover, retail business intelligence software plays a vital role in streamlining retail operations, including inventory management, supply chain operations, and product placements.

By analyzing real-time data on inventory levels, stock shortages, and sales transactions, you can optimize the merchandise offerings, reduce inventory costs, and improve operational efficiency.

Predictive analytics further enables to participate in consumer demand, forecast sales, and make data-driven decisions to stay ahead of market trends and seasonal fluctuations.

BI allows companies to create a data-driven culture and continuously adapt to changing consumer behavior and future trends.

By investing in advanced analytics solutions and fostering a culture of informed decision-making, you can unlock so many insights from your data you could never even think of.

This will allow you to deliver personalized experiences across various channels, including brick-and-mortar stores and mobile apps. Which will ultimately drive customer loyalty and long-term business success in an increasingly competitive landscape.

90% of retail stores operate on very low profit margins. Especially when it comes to high-velocity sectors like:

  • Grocery stores

  • Apparel

  • General Merchandise

On such high volume sales, each percent makes a lot of difference. The most successful companies in the retail industry are successful primarily because they know what works well for them and what doesn't. And they try to double-down on the good.

But how do you know what's good for your store and what's bad? When you have one small store and a limited number of products you sell - napkin math combined with your experience would probably give you the answer.

However, as your business starts expanding and you're opening up multiple locations, start working with more and more suppliers - this is when it's so easy to overlook this mindset and end up in a very low margin, dead loop.

A cycle where retailers find themselves trapped in a pattern of constantly seeking lower prices from suppliers to remain competitive, while simultaneously struggling to maintain profitability due to the resulting low margins.

That's where Business Intelligence comes into play.

In this article I'll walk you through the 7 mistakes I found many retail business owners make while trying to apply business intelligence tools and strategies to their retail companies.

For this article, I'll assume that you already have some BI software in place and a basic understanding of how to use it. If not, you can learn more about different BI tools and their applications here.

Mistake 1: Failing to identify and prioritize KPIs

When Alice approached the cat in Wonderland, she asked, "Where should I go?"

The cat replied, "That depends on where you are going."

Alice said, "I don't know."

"Then," said the cat, "it doesn't matter which way you go."

I really love this quote as it translates to so many things in life. And especially in business. It doesn't matter what kind of BI technology you have in place, if you don't know what your short term, mid term and long term goals are, you will never get the business intelligence working for you.

The biggest mistake that I noticed is however not just not having any KPIs in mind. But rather having the wrong business metrics that you might be trying to improve.

Once you've identified the KPIs, you need to review them on a monthly / weekly basis to understand if the direction you're going with new business ideas is bringing you back whatever you're trying to achieve.

Ideally you should have different KPIs for different areas of your business. Some basic metrics like gross margins, average transaction value and revenue growth should always be in front of you, but there are always specific metrics you should track related to the experiments you're running.

Here's a list of some good metrics that you should definitely consider starting with:

Sales Per Square Foot - a good way to measure a physical store's efficiency. Product Return Rate - The percentage of sold products that are returned by customers. Sell-Through - The percentage of units sold compared to the number of units that were available to be sold. Customer Satisfaction Rate - The percentage of customers who are satisfied with their experience and products. Average Basket Value - The average total amount spent per customer transaction.

Mistake 2: Neglecting to integrate data from multiple sources

Once you set up your BI infrastructure, one of the most important steps should be bringing all the data together in one spot in a way that can be easily analyzed using some software like PowerBI or Tableau.

It might be tempting to rely on some direct integrations with your ERP or POS software, but this approach often falls short. That's because the sources you bring together will be fragmented and give you an inconsistent view of your business.

By taking the time and investing in centralizing your data in a well-designed data warehouse, you open up a world of possibilities. You can combine data from various systems, such as your CRM, Point of Sale software combined with RFID tag data, financial software, etc. to gain a holistic understanding of what is going on in your business.

Now once you have everything in one place, you can easily dive into your KPIs and identify the low-hanging fruits or things that are affecting these KPIs. So you can freely ideate about the potential new things you can introduce to close these gaps.

This alone will set you apart from 80% of your competitors and seriously improve your margins long-term.

Mistake 3: Relying on outdated or inaccurate data

This one is so easy to overlook since it requires some technical know-how which you would typically find by hiring a consultant to do this every once in a while. You might also just have a dedicated team of data engineers that will maintain the quality of your data.

And even if you know for sure that the data you're seeing is fresh and updated, every time you create a new report there's a chance that you will do this incorrectly. This will result in pretty much wrong insights that can harm you a lot if you make some important decisions based on that.

The quick solution for that would be hiring a person who can do this analysis for you on request. It will cost money, and oftentimes take time for them to circle back with results. But at least you will be confident that the data is accurate.

A more sustainable approach in my opinion would be learning how to use BI software yourself (expect this to take some good time) or finding a more user-friendly solution that will still maintain highly accurate results.

A good rule of thumb here is to always double-check the data behind the most important decisions you undertake. Cause this is when the error-risk is at its highest.

Imagine this scenario: you're about to launch a major marketing campaign based on the actionable insights from your BI tool. The data suggests that a particular customer segment is ripe for targeting, and you allocate a substantial budget to reach them.

However, if the underlying data is flawed, you could be pouring resources into a campaign that's doomed to fail from the start.

Hope you get the point.

Mistake 4: Not leveraging predictive analytics

I think one of the main advantages of using retail business intelligence properly is the fact that the more data you collect, the more opportunities you will have to understand the potential future outcomes for different kinds of decisions you make.

Of course it requires some setup. There's no tool out there that will give you solid and reliable forecasting capabilities right out of the box. That's why so many companies skip this part. But I think the ones that don't are the most successful retail businesses out there.

Think Target, Amazon or Costco. All of them have whole departments in charge of estimating business outcomes for changes or market updates. They are really on top of it. That's one of the reasons why these companies are so successful.

And you don't need to have these huge teams to do that. Just think about the potential hire or outsource that can build a sophisticated model specific to your business. The returns would be huge.

These models can help you: anticipate future demand, optimize inventory levels and make more informed decisions about pricing, promotions and product assortments. That's the edge you will have that a lot of companies don't.

Moreover, you can use this data to optimize your store layouts, improve customer service, and even predict which products are likely to be returned. The list is never-ending. This is not easy, but the rewards are totally worth it.

Mistake 5: Failing to communicate insights effectively across the organization

Alright, now that we've covered the main operational mistakes companies make, I feel like the next big mistake lies in the internal company procedures related to business intelligence.

See, you can really nail the process of analyzing your data and gain so many insights into what you should or should not be focusing on. But at the end of the day, unless you're the key decision maker, you will have to onboard other people into your vision of a situation.

What I found actually happens in the real world is that there are people in lower positions like managers or just specialists who decide to dig into some issue and then discover an interesting trend or pattern.

And oftentimes instead of bringing this up they do nothing, thinking that it's either not very helpful/accurate (and they don't want to seem stupid) or nobody will listen to them.

This primarily happens for 2 reasons:

  1. The company has a top-down decision making structure where nobody in the middle can affect anything. This usually results in a very unproductive and stagnant environment.

  2. Upper management is promoting a "Don't bring me the problem without a solution" sort of thing which in reality makes people not share anything if they're unsure about the solution. Even though the best solutions are typically a collaborative effort.

So make sure you're not only investing in the software and talents around your business intelligence but also the underlying structure and culture for other people at your company to follow.

Mistake 6: Underestimating the importance of data security

Everyone I know in companies with more than 10 employees has told me that security of their customers' data is the #1 priority for them.

Yet half of them don't really understand what security means. And if their existing setup is really secure.

It's so tempting to just follow the marketing claims of different websites saying: "YOUR DATA IS ALWAYS SECURE" without doing any due diligence to understand if that's really the case.

And the reasons are obvious:

It takes money It takes expertise It takes time

It's just so much easier to simply trust whatever the website says and then blame them if anything happens.

And yes, maybe legally you can have some chances of defending yourself, but leaked data is leaked data.

Your customers will not think of you as an innocent bunch of people that were bitterly deceived by a malicious software provider.

And they'd be right. It's not their problem. They didn't trust some random website or salesperson. They trusted you - so you should take responsibility for that.

Investing in robust data security measures should be your top priority. This includes things like encryption, access controls, regular security audits, and employee training on data handling best practices.

Yes, it requires an upfront investment of time and resources. But the peace of mind and protection it provides is invaluable.

Don't be one of those companies that learns the hard way. Make data security a core part of your BI strategy from day one.

Mistake 7: Not continuously monitoring and adapting to changes in the market

The final mistake I commonly see is treating business intelligence as a one-and-done initiative.

Companies will invest in setting up a BI system, generate some initial insights, but then fail to iterate and improve over time. They fall into a "set it and forget it" mentality.

But the reality is, both your business and the wider retail landscape are constantly evolving.

Consumer preferences shift, new competitors emerge, economic conditions fluctuate. What worked well last quarter may not be optimal for your business now.

That's why I see the best retail companies continually monitor their KPIs, test new hypotheses, and refine their strategies based on the latest data.

It's an iterative cycle of analysis, action, and adjustment. You learn something new - you apply it and see if it works, and if it didn't, that means you learnt something new again!

So make sure to view BI as a living, breathing part of your business - not a static tool. This will position you for long-term success in this dynamic industry.

Final thoughts

By utilizing the valuable insights from customer experience data, retailers can make informed decisions to optimize marketing campaigns, improve customer service, and enhance the overall shopping experience.

Business intelligence solutions allow retailers to gain a deep understanding of customer behavior, preferences, and interactions throughout the customer journey.

This makes marketing teams create targeted campaigns and personalized product recommendations that drive customer engagement, satisfaction, and loyalty.

Moreover, retail business intelligence software plays a vital role in streamlining retail operations, including inventory management, supply chain operations, and product placements.

By analyzing real-time data on inventory levels, stock shortages, and sales transactions, you can optimize the merchandise offerings, reduce inventory costs, and improve operational efficiency.

Predictive analytics further enables to participate in consumer demand, forecast sales, and make data-driven decisions to stay ahead of market trends and seasonal fluctuations.

BI allows companies to create a data-driven culture and continuously adapt to changing consumer behavior and future trends.

By investing in advanced analytics solutions and fostering a culture of informed decision-making, you can unlock so many insights from your data you could never even think of.

This will allow you to deliver personalized experiences across various channels, including brick-and-mortar stores and mobile apps. Which will ultimately drive customer loyalty and long-term business success in an increasingly competitive landscape.

Start retrieving the insights in your own language

Think about the last time you had a question about your data. How long did it take to answer it?

Start retrieving the insights in your own language

Think about the last time you had a question about your data. How long did it take to answer it?

Start retrieving the insights in your own language

Think about the last time you had a question about your data. How long did it take to answer it?

Copyright © 2024 Docugenie, Inc.

Copyright © 2024 Docugenie, Inc.

Copyright © 2024 Docugenie, Inc.