Artificial intelligence

How can AI help you with Location Optimization

Does your company from time to time need to find the optimal geographical location for something? Perhaps you need to find the best spot for a new store, storage facility, windmill or food truck? To solve this we can use either of two different methods. But first we need to collect a number of location examples. The more examples we have the better the AI will be able to understand what we want it to learn. For each location we need to add as much data as we can find, like:

  • Average outside foot traffic
  • Proximity to highway
  • Windy days per year
  • Number of people working in the area
  • Day of week

If we treat this as a “classification” problem we also need to label every location as “good” or “bad”. We then train the AI on this data. Now we can show the AI new locations and it will tell us (classify) if it considers a particular location to be “good” or “bad”. Pretty neat.

Sometimes the good vs bad classification is not fine grained enough. Then we can treat this as a “regression” problem instead (meaning the AI will predict a number for us). To do this we need to assign a number instead of a label to each location. So a top location could be a 10, a bad location 1, and others somewhere in the middle. We train the AI and it will now be able to give us a number for new locations we show it. This way we are able to rank a list of possible new locations. Even neater!

Why do this

If you need to choose between several possible locations, why would you not want to pick the best one? Of course you want the one you hope will be attracting the most foot traffic, take the least time to drive a truck to, or generate the most electricity. As in many use cases us humans normally have a hunch, but there are too many interacting variables to know for sure. This is where the AI can shine and help us out with its opinion about something based on the examples we have trained it on. So this is a perfect way to make sure your company maximizes its revenue!

How do I get started

You probably have a list of potential locations you are choosing from as well as the example data you want to base your decision on. You need to enrich each item on this combined list with the data you want the AI to consider. This could mean that you have to dig into external data sources, maybe you have to buy statistical data or weather reports, or even just perform lots of web searches. You can even add images of the neighbourhood as input data about a location! Remember, the more relevant data you have, the better the AI’s predictions.

For a retail store chain, here are some example data points to collect:

  • Distance to nearest city center
  • Distance to nearest highway
  • Number of people living within 5 km from location
  • Store size
  • List of all departments in the store

Now when you train an AI with this data perhaps it will find the relationship that “big store, close to highway and has many departments” will be rated as 8. But perhaps it will also find that “small store, close to city center with many people living nearby” is a 10.

For a food truck here are some example data points to collect:

  • Street
  • People working in the area
  • People living in the area
  • Day of week
  • Day of month
  • Season
  • Today’s special was X

This might reveal that “on tuesdays in the summer” going to Street A will always be your best choice.

Remember that you yourself have rated the examples, so the AI will put a higher valuation on the things you have shown it to be more valuable. Hopefully this gives you a better material on which to base your final decision.

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Artificial intelligence

How can AI help you with Customer Segmentation

Often it can be advantageous to divide your customers into groups. On the scale from nothing to full-on one-to-one personalization, segmentation is somewhere in between. Even though segmentation can be a good thing for companies with only a few customers, to really benefit your company probably has a very large number of customers. In AI this is called clustering and is used to create groups of similar customers. Here are some simple example metrics you might manually group customers according to:

  • Most active last day/week/month
  • Most money spent last day/week/month
  • Income
  • Job
  • House vs apartment
  • Address
  • Bought product or service X
  • Number of employees
  • Revenue and profit

However, the really interesting groups are usually discovered by the AI when it is given a bunch of data about each customer and then has to figure out the groups itself! This is when you can unravel completely new insights about your customer base!

So now you have a number of customer segments. What can you do with it? Well, you probably want to contact them about something like:

  • New product or service offerings
  • Discounts
  • Upselling/Cross-selling offerings
  • Special event invitations

I know of companies that refer to some segments as “VIP” and flag them in the call-center support system to make sure they get the best possible support experience.

Why do this

All customers are not created equal. Some are more valuable to your business than others – these are the ones you want to keep by making sure they love you. The customer segment at the other end, well, you don’t want to keep sending discounts and rebates their way, do you?

How do I get started

To find the most interesting segments of customers you need to collect all possible data about them available to you. This could be data from your CRM (Customer Relationship Management) system, your website, your email marketing system, information about the customer location, etc. Of course you can start small with the data you can easily get your hands on, but the more the better. Then you let the AI do its magic and if you are lucky you should be able to reveal some new interesting things about your customers.

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Halftime and time for vacation

We have just passed the second half of 2020, who would have predicted that this is how it would look like? What a huge impact Covid-19 has had on the entire market. Thanks to all employees, customers and partners who have supported us beyond imagination.

It was just about 6 months ago we had our yearly kickoff with our employees at TIQQE. We shared our vision and mission and put our strategy in place together with our employees.

Two months later Covid-19 hit the Swedish market hard, it put us all in a different mode, which was, how do we ensure that we are able to keep all our people during these difficult times. It was a time where we all needed to put our heads together, work as a team every step of the way. This included everything from cutting costs, re-focusing sales, making sure that everyone was well informed and most importantly communication.

We decided our top priority was to first and foremost ensure not letting go of any of our staff and from that decide the priorities and decisions. Sales was more than ever an extremely important part of the solution to rapidly reach out to the market and interpret where the market is, where the customers are and how we align our offerings to the market. Within two weeks, all the actions we had brainstormed where executed on and we found our fit in this uncertain market. Time passed and we worked with different scenario planning, if this happened then we need to do this and if this happens, we should do that etc. Some came true some didn’t. We executed and we broke many eggs, somethings worked some didn’t, we learnt and then moved forward with the next idea.

Was it all doom and gloom? Of course not, it challenged us all, it challenged us to think differently. We had different circumstances than before and we found better and more effective ways to interact with our customers. When it came to our employees, we needed to be creative and we tried many different things and most recently we had a summer party online with all our employees, something we hadn’t even imagined possible 6 months ago.

The result?

Even during these challenging times, we still managed to grow in pretty much every area.

  • 0% turnover of customers & employees
  • 76% growth with new employees
  • Improved diversity from 17% women to 33%
  • 100 % growth with new customers
  • All time high in employee satisfaction (eNPS)
  • All time high in customer satisfaction (NPS)

With this blog we would like to share with you the past 6 months at TIQQE but also to send out a huge thank you to all our customers, employees and partners who have all been extremely important. Thank you and we look forward to work with you and hopefully be able to meet face to face in the near future.

Thank you!