Artificial intelligence

How can AI help you with Product recommendations

Product recommendations is something most people have seen. Some of the most known examples are Netflix movie recommendations and Amazon product recommendations. Entire companies have this as their core offering – helping other companies do it better. But with a little help from AI, doing this well has now become much easier than before. Many companies also have or are planning to incorporate this into their marketing or selling strategies. And for obvious reasons. Showing attractive additional products or packages is often a good way to increase sales.

Traditionally this has been a purely statistical or analytical operation: “on the web page for product X, recommend things that product X buyers also bought”. This is a nice and simple way to start if you have never done recommendations before. But if you wanted to apply AI to this problem – could you? The not so surprising answer is “you could and you should”! As always you want to collect lots of examples to train your AI including things like this:

  • Current product name (on the current web page)
  • Current product price
  • Visitor total time on website
  • Pages visited before current page
  • Geographical location of visitor
  • Total visitor purchase history

With this information the AI will be able to learn the best additional product suggestions for each main product.

Why do this

When your customers have found something that they want, why not help them to find valuable and useful extra products? This could be a great service for your customers that set you apart from your competition. Of course this is by no means an act of altruism – since you will sell more and make more money. So it is a win-win situation!

How do I get started

A good way to get started with this is to pick out some high-volume products. Because the volumes are high you can set up A/B-testing which means that for some customers you show recommendations and for the others you don’t. You now have a way to compare the impact of the recommendations. You also limit the effort required to collect data and train the AI, and you will only affect a part of your business (“small blast radius”). Due to the law of large numbers you can have higher confidence in the results than if your product sells only a small number of items, since as the number of purchases grows the observed effect gets closer and closer to the “truth”.

This could be a rather big project depending on your internal-IT and general IT-maturity. But if you just want to “get it out there” there is a shortcut.

The fast way to get started is just a few steps away:

  • Find the data
  • Train and deploy your model
  • Insert some code in your product webpage
  • Done.

This is such a standardized service that almost all major cloud providers offers this via API integrations, here are a few:

  • AWS Personalize
  • Google Recommendations AI

If you want to learn more about AI please visit:

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.

If you want to learn more about AI please visit:

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.

If you want to learn more about AI please visit:

Artificial intelligence


When Covid-19 impacted the market, we saw many companies taking control of what they could and one of the main actions was to freeze costs.

Even during these challenging times there are still many areas to explore, we see that the patterns of the customers have changed and provides the possibility to invest and develop new revenue streams and in-turn increase turnover and profitability in the short and long-term basis.

This is what we would like to share in this blog and also exemplify.

Lesson from the Airline industry, they work with the data which is internal and external. Internally from the point of view that we have X % booked seats for a certain flight and need to discount or increase costs due to high or low availability. Combining this with external factors such as, which weekday it is, Christmas, New year’s Summer holidays etc. which will impact the price for the customer and of course the other way around in low season when many aren’t travelling the airline tickets tend to be lower.

There are so many industries who have the data but don’t always use it to be able to fill capacity.

I will exemplify:

Imagine the last time you had dinner at a restaurant, you sat down with your friends or family received the menu and ordered your food and drinks. You are next to a big wide window with a fantastic view. You look around the restaurant and see that not everyone has the same view, however everyone is paying the same price. 

If you eat at a restaurant on a Monday or a Friday, it’s usually the same price, right?

Ask yourself would you be willing to pay a bit more for having a great view and booking when it’s Friday or Saturday evening and being sure to get a table? I’m sure in many cases you would. The same thing goes the other way around to also provide an incentive for customers to pay a little less to come before the peak hours or other day of the week at a lower cost. This is something the pubs and bars around the world invented a long time ago and named “Happy hour”.

It’s about collecting the data, analyzing and adjusting and the technology to make this happen has been developed.

This is just one example how to in these challenging times it’s possible to increase revenue and profitability with help of AI/ML.

What would you like to predict and would have an impact on your business to increase revenue and profitability?

Please feel free to reach out to us and we would be happy to listen to your idea and how to turn that idea into a solution.  

Artificial intelligence

Getting started with AWS part two

Last year our colleague Christoffer Pozeus shared how to get started with AWS from a technical perspective covering the different alternatives such as AWS Landing Zone, AWS Control Tower and AWS Deployment framework (ADF). If you haven’t read his blog make sure to give it a read!

In this blog we would like to share some of our experience with some of our recent customers and also what we are seeing in the market and get up and running as quickly as possible with your new AWS environment.

What we are seeing are two main directions customers are taking, first group of customers are moving to the cloud, we are going to migrate from our data centers and go all in AWS. The second group has a lot on-prem, doing a huge lift & shift will not be cost effective and time efficient so they start small and grow from there.If you are a company in situation one or two then you will need to start with building your foundation and AWS in this example there are many ways of doing this. What we are seeing is that many do this themselves and if it’s to build competence within AWS then that is great, but our recommendation is view it as just this, to build competence and not our future foundation that we will over time will move business critical applications which requires a high level of security, cost control and scalability.

Depending on your situation if you have an in-house team who you want to build this competence within or if you want someone else to manage this for you make sure that you receive some help. All from guiding principles, lessons learnt to recommendations. This will enable you and your team and set you up for success and of course avoiding costly troubles later down the road.

We have done this journey many times over the years with all different kind of customers in different shapes and sizes and the question we always get is how long will this take? The boring answer is of course it depends, but the realistic answer is from a few days to a few weeks.

We strongly believe that the best out-comes are when we do things together understanding you as a customer where you are, where you wantto be and how we can support and share our experience and learn together.

To summarize, make sure to set a solid foundation as early as possible which will be sustainable over time!