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Sales forecasting: the challenges of predictive marketing

Let's face it, increasing sales and margins is the Holy Grail of any company. To achieve this, the company must sell its products and/or services while controlling its budget, its stocks and its processes.

Knowing a customer means being able to anticipate his needs and reactions throughout the buying process to enable the company to make the strategic decisions required for its sales objective. Predicting and anticipating customer buying behaviors is the mission and the challenge of marketing.

However, the arrival of Big Data makes prediction more complicated by the amount of data to be analyzed and integrated into a predictive model, not to mention the permanent instability of the market accentuated by the health crisis context.

Using a sales forecasting solution to predict customer behavior meets several challenges. Let's discover them together.

Anticipating customer buying behavior: a competitive challenge

Entrepreneurship has been on the rise for a good decade: strong social integration factors (self-employment) in a context of rising unemployment, synonymous with organizational freedom and autonomy, digital revolution and creation of numerous digital jobs, better work-life balance, etc. Many people have therefore decided to create their own sales or service company.

And this trend still has a long way to go, especially with the health crisis during which many employees discovered the advantages (and disadvantages) of working from home. The world is changing and the landscape is diversifying. The offer on the market of goods and services is large and the online business is growing more and more.

In order to survive, companies must constantly work on their competitiveness.

Sales forecasting as a profitability lever

The main interest for a company to anticipate its customers' buying behavior is undoubtedly the ability to forecast sales. Knowing which products or services will be the most sold over a given period of time allows the company to optimize its processes in order to respond to an increase in activity or, on the contrary, to a decrease in activity.

For example, sales forecasting allows the company to adapt its inventory to the context: plan for a larger stock to reduce the risk of stock-outs during peak periods (and thus ensure customer satisfaction) and sell more, or on the contrary, implement a promotional sales strategy to liquidate a soon-to-be-obsolete product in order to limit losses.

Knowing that storage time is costly for the company (price of storage, immobilization of cash flow, risks of unsold and lost products, etc.), inventory management is one of the most complex aspects of logistics, and even more so in certain sectors, such as food (mass distribution, restaurants, retail, etc.). In order to help companies meet the challenges of sales forecasting and implement their marketing actions, forecasting software exists that is much more precise than the usual CRM software and allows for a more relevant and reliable analysis of information.

Sales forecasting, applied to all sub-activities of the supply chain (transport, handling, purchasing, warehousing, labor, etc.), is therefore an important source of cost optimization, an undeniable competitive advantage for companies. Thanks to a better financial margin, the company can implement a more attractive pricing policy for its customers and/or offer new products or services.

Indeed, cost control allows the company to increase its self-financing capacity, which can then pursue the development of its competitiveness, thanks to innovation for example, without having to rely on external investments (bank credit, among others).

Trend forecasting as a source of innovation

Although companies have always tried to distinguish themselves, or at least to align themselves with the competition, the competitiveness of companies is today an even more important reality and an essential issue for their survival.

Innovation is one of the keys to stand out from the competition. Whether it's a question of proposing a new product and/or service or implementing initiatives to meet the challenges of tomorrow, a precise analysis of customer buying behavior allows us to detect future trends and new needs.

Try to imagine how things would have evolved if the telephone network operators had been able to anticipate the arrival on the market of Free and its policy of low prices. Considered as one of the biggest Internet providers in France today with 420 million euros of profits in 2020, Free might not have been able to take advantage of the situation if the other operators had had the opportunity to develop their low cost packages (Sosh for Orange, Red for SFR or B&You for Bouygues) before the launch of Free's offer, and not after, as it was the case.

But anticipating customer buying behavior in a highly unpredictable market and in the age of big data cannot be done without appropriate marketing tools. With the profusion of data circulating, an Excel spreadsheet and the human brain are no longer enough. More and more companies are becoming aware of the need to use forecasting software.

Why? Because these solutions use artificial intelligence, whose progress in recent years allows its exploitation in the business world. Thanks to the prowess of artificial intelligence and its powerful mathematical calculations, machine learning (the machine learns as it gains experience thanks to the training of algorithms) and scoring tools, a sales forecasting software is able to analyze all the internal and external data of the company in order to accurately predict certain trends, such as:

  • regular, reasoned or impulse purchases;
  • customer appetites;
  • new needs;
  • churn (departure to the competition);
  • threats and opportunities of the market and competition;
  • the company's reputation among consumers;
  • etc.

These predictive models take into account the constraints specific to each business sector, enabling companies to make informed strategic decisions in terms of innovation and competitiveness.

Knowing your customers and their behavior: a loyalty issue

A competitive company attracts customers, that's a fact. But this competitive advantage has to be worked on continuously, because nothing keeps them there if they are no longer satisfied. Moreover, it is more difficult to win back disappointed customers than to win new ones.

This is why every company must know and understand its customers and their reactions in order to keep them loyal, this is what we call their appetence. This requires the knowledge of:
  • their needs;
  • their motivation or interests;
  • their beliefs;
  • their fears and/or obstacles to purchase;
  • their family, social, economic context, etc.

Understanding the reasoning of its customers and their habits allows the company to adjust and to simulate their reactions during marketing actions, for example the success rate of a promotional sale on a particular product or over a given period. Indeed, why launch a promotion at a time when most of your customers are on vacation? Moreover, it is easier for a company to visualize the different impacts of a desired or sudden change (new products or services, change of providers, delivery mode, new pricing, new communication tool or sales support, etc.). This is why sales forecasting software not only predicts, but also simulates the impacts according to a multitude of factors and possible scenarios.

In addition, knowing your customers gives you the opportunity to segment your customers according to different profiles in order to implement distinct and relevant strategic actions according to the targeted population, such as personalized offers or recommendations based on searches made on the company's website or on past sales, or to choose the most adapted communication according to their profile. In this way, this analysis can strengthen the customer relationship and improve the shopping experience.

A better understanding of consumers' expectations also makes it possible to influence their behavior in order to lead them to carry out an action (request for information, making an appointment, purchase, newsletter subscription, etc.) and often, even to create a need among them.

The preceding seems complex and time-consuming, and it is, while at the same time relatively simple thanks to the tools available today. The prediction software allows to automate the forecasts with the help of criteria to be defined in the database. Thus, as soon as there is a combination of several factors, the software sends an alert. The company then only has to implement the appropriate response.

Thus, predictive analysis allows to put customers at the center of the company's strategy to always sell more. By showing them that their expectations are understood, by communicating with them through the right marketing channel according to their profile, by giving them the impression of being special, companies create a bond and build loyalty. Moreover, anticipating buying behaviors allows companies to adapt their processes and reduce logistics costs, but also to gain in performance and service quality, both of which guarantee consumer satisfaction.

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