Predictive Analytics In Retail Marketing, The Use Cases

One of the areas in which prescient examination and prescriptive information investigation can best communicate their actual capacity is in the field of retail showcasing, i.e., advertising methodologies explicit to the retail area. The Retail area creates a ton of information along these lines offering the fundamental establishments for a retailer organization that needs to turn into an information-driven organization.

On the other hand, however, it is precisely this enormous amount of data that often represents “the problem”: with so much data produced and arriving, even in real-time, from various sources and many channels, managing them becomes very complex and, very often, only a tiny part of them is “captured” to make accurate analyzes (with the result that even the insights obtained are partial and it is not sure that they are of value for business objectives and strategies).

To compete with large e-commerce retailers, retailers will increasingly need to learn to manage data and prepare it for analytics, significantly predictive and even more advanced, prescriptive analytics for retail marketing. One of the typical qualms of those who feel the need to adopt advanced data analysis systems (Advanced Analytics) to improve their marketing strategies, and more generally business, is linked to the fact of “not having the necessary data available. “. In reality, very often, the data is there. Still, they are distributed on different sources, not collected in a homogeneous way, not integrated. 

The most common problem is linked to the fact that the data is not managed, not that it does not exist. To exploit the potential of predictive analytics in retail marketing, in principle, certain types of data are needed (although it should be pointed out that the necessary data depend on the specificity of the company, the context in which it operates, and the needs and objectives it sets itself. the business), such as:

1. data of the point of sale,
2. information relating to people’s purchasing behavior,
3. consumer demographics,
4. in-store and online “browsing” traffic flow,
5. other external factors such as the weather.

This already seems to be an enormous amount of data, and retailers fear that they often do not know how and where to “recover” all this data. In reality, the answers can be multiple, but by way of example, we can list some of the most relevant data sources for data analysis in Retail marketing:

1. retailer’s websites (with or without e-commerce),
2. mobile applications,
3. loyalty programs
4. IT systems for points of sale (e.g., payment and shop management systems),
5. supply chain systems,
6. sensors and cameras at the point of sale,
7. social media (conversations, actions, and reactions),
8. promotional campaigns.

Retail Marketing, Five Use Cases Of Predictive Analytics

To better understand how the data can be analyzed to improve some areas of Retail marketing strategies, let’s examine five use cases, i.e., examples of predictive analytics – or rather, of the so-called Retail Analytics – for some specific needs—business (related, as mentioned, to Retail marketing).

Customer Personalization

Understanding your clients’ conduct and consolidating this information with different information segment information is the initial phase in making detailed prescient examinations, such as customizing offers and correspondence and advancement crusades. Today the countless sources of data, from those that come from the retailer’s channels to those “captured” on social media, make it possible to monitor the behavior of people through the different channels they use, for example, to monitor a customer who searches the website of the merchant and then buy the item in the physical store. 

From this simple example, it is easy to understand how the analysis of this data can help retailers personalize offers, communications, incentives, at a very granular level, without having to “shoot the heap” as they did in the past. Predictive analytics can also be used for upselling and cross-selling, modeling ad hoc strategies only on specific audiences.

Customer Journey (Segmentation And Customer Journey)

Client experience planning starts when an individual initially comes into contact with a brand or retailer (even without being a client yet). A perfect world ought to go on forever (don’t tragically believe that the client venture closes with the buy made). The planning of his experience addresses a “drawn-out work.” It is crucial for causing examinations that permit retailers to arrive at their clients and section them and recognize new likely clients.

By breaking down the affinities, a retailer can bunch their client base as per “normal credits” and afterward complete explicit investigates to look at how changed portions of clients have reacted to advertising systems and improvements (via model), as well as make expectations on how and how much another methodology will work (another proposition, a particular advancement) and what impacts it will have on the different client sections.

Behavioral Analytics (Customer Behavior Analysis)

Emerging technologies (IoT, Big Data Analytics, Artificial Intelligence) have accelerated the adoption of behavioral analysis, a very relevant use case in Retail marketing. People-tracking technologies, for example, have enabled retailers to assess the impact of merchandising efforts by analyzing the shopping behavior of customers in stores. Today the points of contact between customer and retailer are varied. 

People use different channels to inform themselves, learn more about a brand or a specific product or service (from websites to mobile applications, up to social media), make purchases in different ways, exploit promotions in different ways, pay and collect products through different channels from these countless choices and behaviors, an incredible amount of data derives that can allow retailers to refine, optimize, improve their business strategies (for example for build customer loyalty and forecast the risks of abandonment).

Marketing Campaign (Management Of Marketing And Communication Campaigns)

As mentioned, the best knowledge of customers (through the collection of data from multiple sources and their predictive analysis) allows retailers to better manage their marketing strategies, also from an operational point of view, for example, in the scope of campaigns. 

Predictive analytics for Retail marketing can help merchants understand not only which messages and content to address to which specific customer segment, but also how to manage campaigns optimally, for example, by choosing the most appropriate communication channels for a particular group of users/customers, sending ad hoc promotions at a specific time and concerning a specific context, defining budgets more accurately and optimizing advertising expenses.

Inventory & Supply Chain (Inventory And Supply Chain Management)

One area that is often overlooked is the back office. Yet poorly managed inventory is every retailer’s nightmare. Supply chains need to be optimized to increase operational efficiency, on the one hand, and not negatively impact the customer experience, on the other. Predictive analytics for retail marketing help answer questions such as “what to store,” “when to store,” and “what and when to remove from warehouses or stores.” 

Making slow stocks of high-turnover products or running out of “popular” products because it was impossible to predict demand (also through the analysis of customer purchasing behavior) represent critical problems that can have non-trivial repercussions on the retailer’s profitability.

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