Meeting the challenges of Retail with Advanced Analytics

advanced analytics per il retail


The Covid-19 pandemic has definitely accelerated and changed consumer behavior: needs, preferences and buying habits have moved very quickly from the physical world to the virtual one. So companies also had to rethink their relationship with consumers in a context in which e-commerce had already radically changed the retail world and the advent of pure players eroded significant market shares. Moreover, brands today are turning to an increasingly “liquid” audience, less tied to the brand and more difficult to retain.

On the other hand, loyalty cards, the use of smartphones, digital tools, virtual assistants, tracking systems (from the web, to IoT, to social media) allow you to have access to a huge amount of data that contains valuable information for business strategy.

Everything that happens in the digital world represents a significant trace to analyze trends, preferences and consumer behavior, identify valuable connections with the company to be found ready and responsive with the right product when the need arises, find new consumer niches, hire them,  lead them to conversion and especially retain them.

How to use Retail Advanced Analytics

The real opportunity today for retailers and companies operating there, is:

  • To access that data,
  • Integrate them with other sources (from CRMs to spatial data),
  • Extract information of high strategic value (meaningful and actionable insights) that can beat the competition in terms of market share.

Advanced Analytics is the preferred tool in retail to predict future events, trends and behaviors even before they occur (predictive analytics), in order to react promptly and take action to change the outcomes to your advantage (prescriptive analytics).

retail advanced analytics business intelligence
Strumenti di Advanced Analytics per il Retail

Omnichannel and BI strategies

In this context, the experiential dynamics of consumers are increasingly fragmented in a buying behavior that winds between physical and digital touchpoints in more and more original paths.

Omnichannel becomes an opportunity to be grasped in two different directions:

  • On the one hand, it allows you to segment at the highest possible level your market, identify patterns of behavior and discover new relationships and purchase dynamics not always evident with traditional tools of Data Visualization and BI;
  • On the other hand, it allows you to respond in a reactive way to what are the insights generated by the consumer (especially on digital channels) not to lose the competitive advantage and seize new opportunities (to reach each potential customer with products, services, promotions and offers that are as customized and oriented to their needs).

So the task of Business Intelligence & Advanced Analytics is to enable physical stores to implement omnichannel strategies through ML and AI tools able to process a high volume of data, in an agile and intelligent way, to extract useful and immediately usable data. At the same time overcoming the difficulty of data fragmentation (heterogeneous sources, non comparable kpi, unstructured data, etc.).

Retail shopping experience

The shopping experience, then, comes out of the physical store and becomes itself an omnichannel experience: the fundamental requirement is to be a unique and memorable experience, not replicable elsewhere, unthinkable to achieve without a Business strategy based on Big Data, Advanced Analytics, digital data integration, in store and out store.

A striking example is that of chatbots, using AI to offer their customers a unique online shopping experience. The Chatbot can become what is the assistance in the physical store, able to accompany and advise the customer in his purchases knowing the history and tastes.

retail shopping experience data analytics
Il Chatbot come strumento di assistenza e di analisi

Advanced Analytics for Retail and GDO

The omnichannel phenomenon therefore allows retailers to have access to a considerable amount of information because it combines the data generated by the physical store with those of the e-commerce world and the related digital channels that feed (web, iot, social).

Data out store

Among the main ones are the geomarketing data, fully inserted in an omnichannel context and always used in the GDO world to support commercial strategies, maximize sales and analyze the competition.

  • Pedestrian
  • Entry rate

Data in store

The physical store can cross-check the receipt and warehouse data with store tracking data (e.g. analyzing customer movements in the store, retail can better understand who its customer is, product associations and improve in-store exposure).

  • Entrances
  • Loyalty rate
  • Average length of stay
  • Visiting frequency
  • Penetration by department
  • Penetration Private Brand (GDO)

Digital data

Digital tracking (web, e-commerce, social media, etc.) allows you to acquire and process all the activities of potential customers to quickly intercept the needs, trends and choices of the consumer.

  • KPIs related to social and web activities
  • Cross channel conversion rate

Moreover, through the Artificial Intelligence it is possible to personalize the choice of purchase creating of the associations between the purchased products (generating of the recommendations and therefore greater sales).

The benefits of a data-driven retail strategy

A data-driven business strategy allows you to gain several competitive advantages, at multiple levels.

At the global level, it makes it possible to define the overall strategy of opening/closing stores, be responsive to consumer choices, distribute product categories, etc.

At local level for each store allows you to understand how to increase the cost of the store, how to improve the assortment, know your customer better from his purchasing choices etc.

How to implement an Advanced Analytics strategy for Retail

Implementing an AA (Advanced Analytics) strategy in your organization is now a matter of survival in the market and no longer of differentiation.

We definitely need specific skills and therefore we see more and more fundamental the figure of the data scientist, although today the market offers end-to-end platforms that aim to bring AI and ML even in organizations not highly trained on data science.

But even before it is necessary to identify the goal for which you intend to start a path of AA and simultaneously check whether the dataset available allows this type of approach.

Realities such as Blue BI allow us to approach the world of AA in a gradual way, making us perceive the real value that AA can bring to the field compared to specific and valuable use cases for the company. In fact, in addition to providing its knowledge of the sector and expertise thanks to the AA team, Blue BI also allows a gradual approach to what is a key theme as technologies as partners of the most performing AI platforms in the market today.

We realize Business Intelligence & Advanced Analytics solutions to transform simple data into information of freat strategic value.


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