Big Data, Machine Learning, Deep Learning, Data Science
, but what are they? Why is everyone talking about it and what benefits can these technologies bring you in your business? Here, we will not explain to you the complex differences between these terms but we will rather bring you concrete use cases through well-known themes of Marketing.
1. Sales prediction
Whether we are talking about the number of visits or the turnover, whether in store or online, whether it is B2C or B2B marketing, our ability to anticipate and predict volumes and amounts of purchasing is a fundamental business need that has not escaped Data Science. If the history of the data is deep enough, modern forecasting algorithms manage to give indicators and signals, with great precision.
These forecasts also make it possible to obtain optimal inventory management, thereby improving the entire supply chain. They anticipate and characterize the staffing needs, whether punctual or permanent, and serve as solid bases for budget preparation.
2. Scoring for targeting
If you consider your customer file as an Excel workbook then scoring only consists in adding a column at the end of the file which gives for each of your customers a note, say from 0 to 100 points. This note can represent different probabilities like that of "opening an email", "clicking on a link", "clicking on a banner" or even "buying within 15 days of your marketing offer". Of course, the closer the score is to 100, the more likely the event will happen and vice versa.
In the case of a marketing campaign, scoring is an eminently ROIs approach since it gives us a forecast of return rates. By anticipating these, it is therefore much easier to control your budget and distribute your campaigns only to the most palatable customers. Scoring makes it possible to have a much higher return on investment and to target the most relevance part of your targets. With just one indicator, you can increase your return rate while lowering the costs of your campaign.
3. Zoning or personalization of editorial content
In this era of hyper-personalization, marketing is no exception. Data Science has indeed taken hold of a subject historically reserved for A / B testing: zoning or "placement of editorial content".
Imagine that you want to place an editorial insert on your website or a mailing campaign. You have two options (A or B), which one should you choose? Which will have the most impact? The A / B testing will answer "test both on small volumes and you will see the results". Where the maneuver is much more complex is when you have more than 2 possible content, we are obviously talking about A / X Testing! Same issue, same question: which communication is the most relevant?
Where traditional A / X will struggle to recommend specific editorial content for a given sub-population, Data Science will be able to highlight the differentiating elements thanks to its machine learning algorithms natively designed for this purpose. You therefore get a multiple response specifying which content for which sub-population. The customer experience will therefore be improved because of more personalization.
4. The recommendation engine
If you are an Amazon customer, the recommendation engines must speak to you. They are often hidden behind sections like "frequently purchased products together", "customers who viewed this item also viewed / purchased". Likewise, Netflix gives a rating for the content that you would probably be delighted to watch. Spotify adopts the same techniques for its playlists.
In short, as the name suggests, a recommendation “engine” is a tool for listing the different products in the catalog that could appeal to all of your customers. Its use is very varied: it can be an insert in a communication as well as a web interface. In terms of profits, this engine mainly targets additional sales (cross-selling or X-selling), but also upmarket or up-selling. To be able to implement this type of technique no: you need a history of consumption and consultation on a large part of your customers.
5. Dynamic pricing
Dynamic pricing is a modern method of adjusting prices according to demand, supply and competition. It is already widely used in the hotel and travel industries. Many of you know that the price of a plane ticket varies depending on the period and time you book it. In the same way for retail, it is possible to define a model taking into account transactional data, product descriptions, past promotions, customer reviews, competition prices, inventory and supply chain data or geographic data. You then have to define the constraints and objectives such as profit maximization, customer loyalty or targeting a customer niche. Then comes the phase of modeling of the data to be optimized and impact simulations on customers. Machine learning and Data Science make it possible to take into account a large volume of data input to these algorithms and therefore to have increased performance and relevance.
6. The conversational agent or chatbot
A real star in customer interaction and considered as a channel in its own right, the ChatBot is responsible for simulating human intervention during an exchange with the customer. ChatBots or conversational agents can be implemented both in writing as with Messenger, the famous instant messaging application from Facebook, or orally with Alexa or Google Home. But concretely what are they used for and how can they improve your customer relationship? Currently, one of the most tangible implementations is the completion or substitution of Frequently Asked Questions (FAQ). It's about providing a quick answer to the questions most frequently asked by your customers. Two beneficial effects should be emphasized: a significant reduction in the burden on employees assigned to customer relations and high availability with 24-hour support. To be able to build this kind of robot effectively, it is essential to contact a data science company and have the questions asked by customers as well as satisfactory answers to these questions.