Artificial Intelligence, Machine Learning, and Your Company

If you are living on planet earth, you likely have not missed the advancements in artificial intelligence (AI) and machine learning (ML). Seems like they’re everywhere — from your own personal assistants (Siri, Alexa), self-navigating automobiles, image processing algorithms that can analyze x-ray scans or set your photos based on family members, in military applications — the list goes on infinitely.

What do AI and ML mean to your small business?

The question is, what do AI and ML mean, and can these buzzwords affect your company? Let’s go with an intuitive definition: AI tools enable your computer, site, or point of sale (POS) to do things that look very smart, things that you would normally think need human intelligence, and sometimes much more. A subset of these tools, ML algorithms, allow computer software to scan information, learn from it, and apply valuable decisions. Combine these tools together with the advances in large data collection and analysis, and you have got an wonderful toolbox that will help you with your company.

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As a restaurant or shop manager, you are probably most interested in how these tools can affect your bottom line. There are lots of indirect approaches here where AI can help. By way of instance, you may use 24/7 online chatbots to enhance your customer service. Better service finally means more clients. In this post, however, we will take the head-on approach. Let us see how AI can make your marketing efforts create more revenue!

Marketing is all about getting the right message to the ideal individuals to encourage them to purchase your goods. That is why it works best when you understand your audience. The more you understand about what motivates your customers, the better you are able to personalize your offer.

Which brings us to the subject of the post: How do AI tools help me optimize my campaigns for maximum revenue?

Let us look at two situations:

  1. You are starting a”25% off” effort. Who should you target? On the one hand, you may want to show gratitude to your most loyal customers. But your most loyal customers may have already planned to come through the campaign period anyhow. By rewarding them, you have simply lost 25 percent of your earnings. That is a losing campaign! So, how about targeting members who wouldn’t have come otherwise, trusting the reward will change their minds? Amazing idea, but how can you find them? Now, you’d begin imagining: Perhaps target members who have not visited for months. They probably won’t arrive soon. Or maybe members who came last week. What are the chances they will come again in such a short timeframe?
  2. That you need to market a new product with a”Spend over $20, and get this thing for free” reward. You do not really care that redeems this benefit, but it must be during the following week to provide the new product a wonderful kick start. This time, it would be a waste to send the deal randomly since most customers are not going to come so soon. You don’t need to spam them with an immaterial deal. How do you find clients that are most likely to come so that you can reap the benefits of a high redemption rate?

The above scenarios demonstrate the challenges of fitting the ideal reward to the ideal audience for the desired result. It is the toughest challenge in incentive-based advertising! Fortunately, this is a classic problem for ML algorithms. Let’s see exactly what you will need to make this work.

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Developing a revenue-optimized effort requires:

  1. A buy database for extracting customer visit patterns as input to the ML algorithm
  2. A machine learning toolbox for predicting future trip probability from historical purchase information
  3. A customer database to your clients’ preferred communication method (telephone number for SMS, app for push notifications, emails, etc.)
  4. A client participation platform for sending rewards and enabling customers to redeem them in the POS

With these tools, technical users can export the purchase data from their POS (as an instance, as a normal CSV file) and import it into a cloud-based ML platform (one example is Google BigQuery ML). A logistic regression algorithm would enable you to utilize member purchase history to forecast the probability of member visits throughout your campaign period! Armed with this information, you could pick the appropriate people to target, and import it into the client engagement platform to ship the benefit.

Less technical users would probably prefer to use an engagement platform with this magical built in, integrated with their POS. So, no dull export/import processes involved. Just pick the rewards you want to distribute, and allow the AI find the ideal reward for every member to maximize your revenue.

AI and ML Solutions

Como has just established Comillia AI, a new motif from the consumer engagement platform’s merchandise for all AI- and ML-related capabilities. Comillia AI Campaigns is the most recent feature in this subject for launch hassle-free, automatic, weekly campaigns. Just pick the rewards and the amount of weekly members to aim, and Comillia will establish your revenue-optimized weekly campaigns for you!

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