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  • 9 February 2021

Customer Analytics: solutions offered by data analysis.

Customer Analytics: solutions offered by data analysis.

Customer Analytics: solutions offered by data analysis. 1024 536 3rdPlace

written by Maurizio Crisanti

Customer analytics: using customer data to improve sales.

Customer Analytics (CA) can be defined the process used by companies to acquire and analyze customer data. Today, no company can escape the analysis of user data, which are essential for guiding marketing, product development and managing logistics as well.

Customer Analytics: why to use it.

Knowing the customer, his behaviors and needs allows those who design products or services to respond to his needs, offering exactly what users really want and it leads to obvious economic advantages both in business management and, above all, in sales. The main goal is to increase conversions, targeting customers with the right product at the right time and with the right price, improving the user experience and leading the customer journey to conversion. Customer Analytics affects business decisions and the organization of internal processes, allowing you to increase revenues also through cost optimization.

Digital transformation determines customer centrality.

The market of goods or services has changed in recent years. Digital technologies have made it possible to collect data relating to markets, customers, web searches, and user interests. The digital transformation path undertaken by many companies has highlighted how the development of a successful product is absolutely linked to users’ needs. The customer and her needs are therefore at the center of the company business and guide its strategic choices. But how to follow customers’ needs?

Currently, it would be completely useless, for example, to spend resources on researching and producing a photographic camera for families, when the data reveals that users take photos mainly using the smartphone. Even a large brand that until yesterday produced unparalleled optics will have to take note of this change of habits and consequently orient corporate decisions.

Today, it is the customer who determines the success of a product or service because he buys what meets exactly his needs, whether immediate needs or desires to be satisfied. Companies that still focus on the product, even with important continuous development, run the risk of putting into production goods that no longer meet the needs of customers, which no one will buy.

Attualmente sarebbe del tutto inutile, ad esempio, impiegare risorse per la ricerca e la produzione di una macchina fotografica per famiglie, quando i dati rilevano che gli utenti scattano foto utilizzando prevalentemente lo smartphone. Anche un grande marchio che fino a ieri produceva ottiche impareggiabili dovrà prendere atto di questo cambio di abitudini e orientare conseguentemente le scelte aziendali.

Oggi è il cliente a determinare il successo di un prodotto o servizio, perché acquista ciò che risponde alle sue necessità, siano bisogni immediati o desideri da soddisfare. Le aziende che mettono ancora al centro il prodotto e il suo continuo sviluppo, rischiano di mettere in produzione beni non più rispondenti ai bisogni dei clienti, che nessuno acquisterà.

The advantages of Customer Analytics

The strength of customer analysis is that through the results it is possible to make data-driven decisions: not simple management insights, but strategic choices based on certain and measurable elements. Customer Analytics is therefore specifically useful for:

  • Digital Marketing: channels and campaigns will be driven by data, consequently improving ROI and thus reducing costs. It will be possible to design and automate customized campaigns, tailored to the customer, offer bundled products, or communicate more convenient proposals, with a view to the Next Best Offer or Bundle ones.
  • Pricing policies: the price is determined on the basis of data relating to the demand, expectations of customers and the proposals of the competition. It involves acting on the price by relating it, for example, to the purchase history or to the interests of the user.
  • Stocks and logistics: customer data and the propensity to purchase some products compared to others help plan the replenishment of the warehouse by managing it in the best possible way and anticipating customer requests.
  • Speed ​​of production and delivery: the ability to predict which products will be sold the most, when and where they will have to be delivered, directs production to anticipate demand.
  • Greater profitability: Customer Analytics allows you to develop more competitive prices, have lower costs and increase sales are the result of targeted marketing efforts.
  • Customer loyalty: offering products or services at the right price increases customer satisfaction and helps build customer loyalty. Combined with other loyalty activities, such as email marketing or loyalty programs, which are essential for consolidating the customer portfolio.

Discover the solution to ACTIVATE your data

How the Customer Analytics process works

The phases of Customer Analytics can be summarized in 5 steps.

IDENTIFY GOALS AND DATA SETS TO COLLECT

What are the objectives? Knowing the customer, increasing sales, opening up new markets … it is important to ask yourself all the questions to have clarity on the results you want to achieve through the analysis of actual and potential customer data.

What customer data do you need to capture? For example, what products do they buy, when do they take orders (in the evening? on weekends?), which channel do they prefer to ask for information, what is the propensity to buy relating it to promotions, what age, year and in what geographical area do they live etc…

These are just some of the information that can be useful in Customer Analytics.

DETECT THE METRICS THROUGH THE IDENTIFICATION OF THE MOST INTERESTING KPI

After establishing which KPIs and performance indicators do you want to calculate, and after acquiring the data, not only you need to archive them, but you need to transform them into easily intelligible reports. Customer analysis metrics help the company to verify market trends and user response to the products or services offered. Today it is possible to monitor data in real time, with platforms such as DataLysm, capable of effectively monitoring metrics and interpreting them. These dashboards help the company make key decisions in a timely manner to increase sales.

ANALYZE THE DATA

The Customer Analytics tool then carries out an exploration, cleaning and preparation of customer data to analyze and compare segments. In this phase, in relation with the business objective, the analyst designs the data model, exploring various possibilities in modeling and selecting the best performant model.

At this stage, after the creation of the predictive or classification model, the analyst will check whether this has been optimized and whether all the variables in the model are statistically significant.

ACT ON THE BASIS OF THE DATA OBTAINED FROM CUSTOMER ANALYTICS

Predictive models represent a snapshot of what is currently happening in the business and what is more likely to happen in the future. Due to the dynamic nature of the models, it is essential that the company monitors changes so that it can adapt their marketing and sales strategies accordingly.

AUTOMATE PROCESSES

When the model is finally the best possible one, it is time to automate the system by integrating customer data, internal processes, analytical techniques and models to meet business objectives. In this way it is possible to reach the customer with personalized campaigns that are automatically designed based on the purchasing behavior.

Conclusions

A dynamic Customer Analytics system allows you to know your customers and their behaviors, reduces customer analysis costs and improves profitability. In addition, the CA improves the customer experience thanks to timely personalized offers, building loyalty. The effectiveness of data-driven marketing actions is evident because it improves ROI, thanks to the customization of offers based on customer needs, making the use of Customer Data Platforms, such as DataLysm, a valuable investment for the company.

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