LET'S TALK ABOUT...

Using data for growing your business with Edwina Dunn OBE

'Let's Talk About' is our series of videos offering practical digital skills advice to help you grow.

Edwina Dunn OBE is one of the most successful leaders in the data industry, with a career of delivering transformational business change.

She was CEO and co-founder of dunnhumby, which revolutionised the world of retail and consumer packaged goods, and pioneered Tesco Clubcard and other global loyalty programmes.

In this video, Edwina is talking about Data and how it can transform your business.

Hello, I'm Edwina Dunn. Throughout my career, I've worked in data science. I have founded and evolved a number of companies to explore rich, new, powerful data. With the purpose of growing businesses. Let's talk about data and how it can transform your business.

I, several years ago, founded a company called Dunnhumby with my husband Clive Humby. And that began when we really started Tesco Clubcard and it changed the fortune of Tesco. And from there, our business was propelled into a global enterprise. By creating Clubcard, we could link a customer's ID to their transactions. By knowing your customers, by knowing what they bought, what they liked, how often that translated into better business, better strategies, better engagement. There's a direct correlation between knowing your customers more and making more money. There's a very famous quote that we've always treasured, which is when Ian MacLaurin, now Lord MacLaurin was CEO of Tesco. After we did our first analysis of the data, he paused, and he said, what is incredible is that in three months, you've told me more about my customers than I've understood after thirty years. And that was just such a huge statement because he was a man, top of his game, and he realized that data could shine a light and reveal things that the brain can't do on its own. And even better, it’s secret from your competitors because they don't understand how you've worked that out and who you're testing it with. So it's almost like a very secret weapon where you're finding out what works best without actually announcing it to everyone. So let's start with what is data. Data is really, really simple. There's personal data and there's attribute data. Personal data identifies an individual. It's your name, it's your address, it's your email, it's your social handle. The things that connect us directly to you as an individual. Attribute data is data like, what did you buy? Where did you buy it? When did you buy it? All of those are transactions, and we attach transactions to personal data. So if we think about what technology and data is trying to achieve here, it's really trying to replicate the intimacy that shopkeepers had in the old days.

So they would know when Mrs. Jones or Mrs. Smith came into the store, what she was likely to buy was often a repeat of what she bought the week before. And so that knowledge was really instructive in how the shopkeeper responded, what he'd prepare for her, what he would entice her with, to buy in addition to what she normally bought. And most businesses don't have that same local community and intimacy with their customers. It's an incredibly powerful tool for getting closer to your customers for making them feel, you know them and they're special. And that you're actually talking to them in a truly personalized way, as opposed to just calling them Mrs. Smith or Mrs. Jones. The first classic example of data is what we call proprietary. So proprietary data is the data that is yours. Data that you hold within your organization, about your customers, that can be really sensitive. Everything from their name, their email, their address, through to their transactions and anything else they've ever told you about themselves. That's proprietary data. You can't own a customer, but you can own your knowledge about that customer because they've granted you that knowledge. So in a sense, if you don't use proprietary data, then you're kind of not using that special relationship you have with your customers. No one else can mimic it, so it gives you that unique insight. The second type of data is reference data. Reference data allows you to give context to your own data. Do you know the businesses around you? Do you know what they sell? Have you been there? Have you seen what service is like? An awful lot of businesses are comparison retailers. So a consumer will go into one store and then try another, and then try another, until they decide which one gives them exactly what they want.

If you're not aware of what's around you, then you are really flying blind. When it comes to reference data, get out there, have a look, be aware of what surrounds you and understand what your competition is doing and how good they are at doing it. So a third type of data is licensed data, and that simply means that someone else has compiled it, someone else has invested in it, but it's available to you. YouGov is one of the best sites to have a look at masses of really good, insightful data. How many people live, work in an area, the socio-economic characteristics of an area, how many people come out of tube stations every day? So if you're a sandwich shop, you want to know how many people are going to go past my outlet in that moment. Or if you're a restaurant, you want to know how many workers, how many residents. And now from this data, from the license data, you could see how many people are the latent demand that you're tapping into.

And of course from your proprietary data, you can see where you sit in that ecosystem. So these are layers of data that all become incredibly powerful in showing you where you sit and how you relate to all of those different dynamics. So data science is really about pattern recognition. You're trying to find groups of people who behave in a certain way, where that way is distinctive and different to other people. So it's about finding these passions, finding these differences, and then applying them really well to your business from ranging to promotions, to communication. And the amazing prize here is that the better you use data, the more you respect data, the better the performance of your business is going to be, and the more valuable your business. If we take a business like a gym, one of the things that we would typically see is that after Christmas, everyone feels really bad and really guilty because they've eaten too much and they've put on a few pounds and they will sign up for the gym.

And so there's a huge surge in January for gym membership, but it won't necessarily follow through, so then there will be a lot of cancellations. So one of the things that gyms can do is recognize that there's a really wobbly period through January, February, March, and actually start to encourage people with extra enticements. And then suddenly that incentive gets people over that really difficult period. So another example is an online clothing, fashion retailer, and here there is such a typical behavior of discount, discount, discount. Whereas actually a lot of customers want different things. So if we take a hundred people and we look at how those people might be different, of that hundred 20% are under 20. And their needs will be completely different to the other 20 or 30% that might be over 50. And so the same offer is not going to work.

So if we send the same message to every one of them, it's disrespectful and it's lazy. And it's not showing that we've seen by their behavior, that they're different. And that's really what personalisation or customisation should actually be about, which is, I know you a little and I've made the effort to respond to you in a way that shows I care. Well, knowing your customers, knowing them in detail, gives you tremendous competitive advantage. You only see this if you analyse your data. If you rely on your memory, we remember the people that make an impression on us. So we have a very selective memory, which means we have an inherent bias as to what we think about our customers. We remember the nice ones, we might remember the bad ones, but we remember them selectively. Data science allows you to unpick it and then structure a plan about how to go back to each and every one of them.

So now we have the tricky part, which is having collected all of this, what do we actually do with it? How do we start analysing and understanding what we have? But getting started can be really quite easy. There are a lot of very, very simple tools out there, which allow you to analyze data simply. An Excel spreadsheet is one of them. That's one of the simplest and most powerful analytic tools. When you use social media, there are a lot of simple, free analytics platforms that you can use. Google search, Google trends, that kind of thing. So each of the social media platforms perform and set out their store very differently. You have to play by the rules with the tools of that social media platform. If Facebook is your preferred place, then you have to familiarize yourself with those. If Instagram is your favoured route, then there are different tools and rules there.

There are also some things that enable you to start taking action. If you're taking some analysis and you want to turn that into communication, you can use something like MailChimp, which is really designed for small businesses. When you start to become a bigger business, you might start to look at some of the more sophisticated platforms. So Salesforce is a communication activation platform that can be relatively simple, to really big. So data is a strategy that affects everything. From pricing, new product development, supply chain, even communications, the whole set of responsibilities that you have, knowing what people want, will have an impact on all of those. And it will help you to do all of them better. You'll save more money and you'll make more money. So in conclusion, this is a new journey, it's a new opportunity, but it's one that's proven to deliver results. So good luck and enjoy it.

'Let's Talk About' is our series of videos offering practical digital skills advice to help you grow.

Edwina Dunn OBE is one of the most successful leaders in the data industry, with a career of delivering transformational business change.

She was CEO and co-founder of dunnhumby, which revolutionised the world of retail and consumer packaged goods, and pioneered Tesco Clubcard and other global loyalty programmes.

In this video, Edwina is talking about Data and how it can transform your business.

Hello, I'm Edwina Dunn. Throughout my career, I've worked in data science. I have founded and evolved a number of companies to explore rich, new, powerful data. With the purpose of growing businesses. Let's talk about data and how it can transform your business.

I, several years ago, founded a company called Dunnhumby with my husband Clive Humby. And that began when we really started Tesco Clubcard and it changed the fortune of Tesco. And from there, our business was propelled into a global enterprise. By creating Clubcard, we could link a customer's ID to their transactions. By knowing your customers, by knowing what they bought, what they liked, how often that translated into better business, better strategies, better engagement. There's a direct correlation between knowing your customers more and making more money. There's a very famous quote that we've always treasured, which is when Ian MacLaurin, now Lord MacLaurin was CEO of Tesco. After we did our first analysis of the data, he paused, and he said, what is incredible is that in three months, you've told me more about my customers than I've understood after thirty years. And that was just such a huge statement because he was a man, top of his game, and he realized that data could shine a light and reveal things that the brain can't do on its own. And even better, it’s secret from your competitors because they don't understand how you've worked that out and who you're testing it with. So it's almost like a very secret weapon where you're finding out what works best without actually announcing it to everyone. So let's start with what is data. Data is really, really simple. There's personal data and there's attribute data. Personal data identifies an individual. It's your name, it's your address, it's your email, it's your social handle. The things that connect us directly to you as an individual. Attribute data is data like, what did you buy? Where did you buy it? When did you buy it? All of those are transactions, and we attach transactions to personal data. So if we think about what technology and data is trying to achieve here, it's really trying to replicate the intimacy that shopkeepers had in the old days.

So they would know when Mrs. Jones or Mrs. Smith came into the store, what she was likely to buy was often a repeat of what she bought the week before. And so that knowledge was really instructive in how the shopkeeper responded, what he'd prepare for her, what he would entice her with, to buy in addition to what she normally bought. And most businesses don't have that same local community and intimacy with their customers. It's an incredibly powerful tool for getting closer to your customers for making them feel, you know them and they're special. And that you're actually talking to them in a truly personalized way, as opposed to just calling them Mrs. Smith or Mrs. Jones. The first classic example of data is what we call proprietary. So proprietary data is the data that is yours. Data that you hold within your organization, about your customers, that can be really sensitive. Everything from their name, their email, their address, through to their transactions and anything else they've ever told you about themselves. That's proprietary data. You can't own a customer, but you can own your knowledge about that customer because they've granted you that knowledge. So in a sense, if you don't use proprietary data, then you're kind of not using that special relationship you have with your customers. No one else can mimic it, so it gives you that unique insight. The second type of data is reference data. Reference data allows you to give context to your own data. Do you know the businesses around you? Do you know what they sell? Have you been there? Have you seen what service is like? An awful lot of businesses are comparison retailers. So a consumer will go into one store and then try another, and then try another, until they decide which one gives them exactly what they want.

If you're not aware of what's around you, then you are really flying blind. When it comes to reference data, get out there, have a look, be aware of what surrounds you and understand what your competition is doing and how good they are at doing it. So a third type of data is licensed data, and that simply means that someone else has compiled it, someone else has invested in it, but it's available to you. YouGov is one of the best sites to have a look at masses of really good, insightful data. How many people live, work in an area, the socio-economic characteristics of an area, how many people come out of tube stations every day? So if you're a sandwich shop, you want to know how many people are going to go past my outlet in that moment. Or if you're a restaurant, you want to know how many workers, how many residents. And now from this data, from the license data, you could see how many people are the latent demand that you're tapping into.

And of course from your proprietary data, you can see where you sit in that ecosystem. So these are layers of data that all become incredibly powerful in showing you where you sit and how you relate to all of those different dynamics. So data science is really about pattern recognition. You're trying to find groups of people who behave in a certain way, where that way is distinctive and different to other people. So it's about finding these passions, finding these differences, and then applying them really well to your business from ranging to promotions, to communication. And the amazing prize here is that the better you use data, the more you respect data, the better the performance of your business is going to be, and the more valuable your business. If we take a business like a gym, one of the things that we would typically see is that after Christmas, everyone feels really bad and really guilty because they've eaten too much and they've put on a few pounds and they will sign up for the gym.

And so there's a huge surge in January for gym membership, but it won't necessarily follow through, so then there will be a lot of cancellations. So one of the things that gyms can do is recognize that there's a really wobbly period through January, February, March, and actually start to encourage people with extra enticements. And then suddenly that incentive gets people over that really difficult period. So another example is an online clothing, fashion retailer, and here there is such a typical behavior of discount, discount, discount. Whereas actually a lot of customers want different things. So if we take a hundred people and we look at how those people might be different, of that hundred 20% are under 20. And their needs will be completely different to the other 20 or 30% that might be over 50. And so the same offer is not going to work.

So if we send the same message to every one of them, it's disrespectful and it's lazy. And it's not showing that we've seen by their behavior, that they're different. And that's really what personalisation or customisation should actually be about, which is, I know you a little and I've made the effort to respond to you in a way that shows I care. Well, knowing your customers, knowing them in detail, gives you tremendous competitive advantage. You only see this if you analyse your data. If you rely on your memory, we remember the people that make an impression on us. So we have a very selective memory, which means we have an inherent bias as to what we think about our customers. We remember the nice ones, we might remember the bad ones, but we remember them selectively. Data science allows you to unpick it and then structure a plan about how to go back to each and every one of them.

So now we have the tricky part, which is having collected all of this, what do we actually do with it? How do we start analysing and understanding what we have? But getting started can be really quite easy. There are a lot of very, very simple tools out there, which allow you to analyze data simply. An Excel spreadsheet is one of them. That's one of the simplest and most powerful analytic tools. When you use social media, there are a lot of simple, free analytics platforms that you can use. Google search, Google trends, that kind of thing. So each of the social media platforms perform and set out their store very differently. You have to play by the rules with the tools of that social media platform. If Facebook is your preferred place, then you have to familiarize yourself with those. If Instagram is your favoured route, then there are different tools and rules there.

There are also some things that enable you to start taking action. If you're taking some analysis and you want to turn that into communication, you can use something like MailChimp, which is really designed for small businesses. When you start to become a bigger business, you might start to look at some of the more sophisticated platforms. So Salesforce is a communication activation platform that can be relatively simple, to really big. So data is a strategy that affects everything. From pricing, new product development, supply chain, even communications, the whole set of responsibilities that you have, knowing what people want, will have an impact on all of those. And it will help you to do all of them better. You'll save more money and you'll make more money. So in conclusion, this is a new journey, it's a new opportunity, but it's one that's proven to deliver results. So good luck and enjoy it.

About the author

About the author

Edwina D. Dunn, OBE is an English entrepreneur in the field of data science and customer-centric business strategy.

Since 2014, she has been the Chief Executive Officer of the consumer insights company, Starcount.

She is also the Founder of the Female Lead: an educational charity dedicated to celebrating the achievements and diversity of women. 

There's a direct correlation between knowing your customers more and making more money.

Edwina D. Dunn, OBE

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