What Is the Real Added Customer Value with Intelligence for Businesses?

What Is the Real Added Customer Value with Intelligence for Businesses?

 

Customer value with Business intelligence, which is based on advanced AI technologies, opens unprecedented perspectives in terms of understanding needs and customer expectations.

While 67% of brands say they measure customer satisfaction, their approach to understanding the “voice of the customer” remains superficial. Most analyze only samples of data and know what is being said about them, without understanding why. Without this analysis of the hidden reasons for satisfaction or dissatisfaction, how to improve the customer experience and generate ROI? Customer intelligence, which is based on advanced AI technologies, opens new perspectives.

Measure the profitability of your customers:

The Customer Lifetime Value or customer value is a strategic way to target its best customers in order to retain them but also to attract new ones.

The customer experience becomes central for B2B and B2C businesses. Providing a premium customer experience has become essential to retain customers and increase profits. Knowing and mastering its Customer Lifetime Value, commonly known as Customer Lifetime Value or customer value, allows you to strategically target the best customers in order to retain them, improve their services and attract new customers.

What is Customer Value?

The CLV is an estimate of the sum of the profits a company expects to make throughout its relationship with a customer. Knowing exactly how much each customer can generate business to the company can determine the amount to invest to attract and retain. Combined with the customer acquisition cost, this indicator maximizes the commercial and marketing strategy.

Understanding needs and customer expectations are vital for all businesses today. It is the customer value who makes and breaks the reputation of brands. And regaining the trust of a dissatisfied customer is expensive, both in terms of time spent resolving its difficulties but also in terms of loss of earnings related to his departure, not to mention the negative word of mouth.

How to calculate the customer value?

There are many ways to calculate Customer Value. Various variables can influence the calculation such as the churn rate, the discount rate, profit margins, royalty costs, and so on. It is, therefore, appropriate to adopt the formula to the products or services sold. However, the most classic calculation of the Customer Value Life can be broken down as follows:

CLV = (Average order value) x (Number of sales) x (Average duration of the relationship)

What are the benefits of CLV?

The Customer Value Life provides valuable information on the life cycle of each customer, its profitability, the best marketing and commercial actions to offer and the potential it holds at each stage of the customer relationship. Thanks to this information, the Marketing and Sales Directors are able to prioritize the customer segments to contact in priority, the budget to invest for each action and, by similarity relation (or “look-alike”), to deduce when the client may be curling or be ready to buy a new product.

In fact, companies have, in recent years, invested heavily in programs to listen to the customer’s voice. 2/3 [1] of brands declare measuring customer satisfaction, in one way or another.

The Limits of The Current “Voice of The Client” Approaches

Beyond listening to the customer’s voice in the “social web” sphere, the most common approach is to collect direct customer returns via satisfaction questionnaires or surveys, after an interaction (purchase of a product for example). Although this method generates a lot of customer data, it is superficial. It is based on a very controlled approach by the brand.

At a minimum, it sets the following limits: questions (and answers) are often oriented or even formatted; it offers only partial vision because not all clients respond; returns may be inaccurate or out of date if they are collected too late after the interaction; finally, it does not offer an overview of the conversation and the course customer because it focuses on specific “solicited” topics.

In addition, this method does not take into account other sources of the customer’s voice (email, chat, telephone, mentions on social networks, consumer opinion site), nor deduced data (such as data from the website linked to the customer journey). So, what is the ROI of these steps?

The brands recognize themselves the limits of these steps of a collection of the Voice of the Customer. For 40% of them, the current tools are unreliable. The analysis of the verbatim is done very often on the basis of “keywords” and not on broader themes taking into account the context, the feelings or the emotions.

Above all, they have difficulty converting VoC data into directly exploitable information that could generate ROI. In many companies, these customer voice programs focus primarily on indicators such as the Net Promoter Score (NPS), the Customer Recommendation Index (CRI), the customer satisfaction score (CSAT), the Customer Scoring effort (CES) or the probability of recommendation (LTR), according to their upward or downward trends.

These indicators establish whether clients are satisfied or not and to what extent they recommend the brand, but they do not explain why, or what should be improved operationally to meet their expectations. Similarly, VoC data often remains siled within the departments that conduct these analyzes, while they may be useful to a large number of departments in the enterprise. A study shows that less than 25% of brands are able to convert VOC data into business process changes. VOC data often remains siled within the departments that conduct these analyzes, while they may be useful for a large number of departments in the enterprise.

The 3 Pillars of An Effective Voc Program

 

An effective and “right” strategy of listening and analyzing the Voice of the customer must be articulated around three complementary axes:

  • Adopt a holistic approach to capturing incoming spontaneous conversations, all channels combined (email, chat, speech to text, social media, etc.) but also feedback, to analyze ALL the voice of the client
  • Focusing on understanding WHY my client said that or reacts like that, rather than just knowing what was said. It is the analysis of causes that will make the decisions that are required at the operational level
  • And irrigate all departments of the company whose business and processes have everything to gain from the exploitation of the voice of the customer to improve the experience throughout the customer journey (marketing, customer relations, loyalty service, e-commerce, IT, operations, commercial).

Being interested in why enables companies to leverage customer intelligence to implement critical changes to improve operational efficiency and therefore customer retention, acquisition, sales and customer experience.

The Key Role of AI in Providing Business Intelligence to Customers

customer value

 

The most advanced artificial intelligence (AI) technologies on the market, such as automatic language processing (NLP), text analytics and machine learning, offer particularly rich prospects for democratizing customer intelligence in the enterprise.

They help to understand the meaning of customer verbatim and reveal the real reasons for dissatisfaction by identifying major themes, intentions,and risks and even go beyond the text to extract data on the emotions of customers. These powerful systems are driven by a list of topics relevant to each business line and automate the analysis of large volumes of structured and unstructured information, providing a true mapping of the experience that lives the customers.

Value Usable by All Departments of The Company

This new generation of solutions provides customer intelligence and democratize the customer’s voice throughout the enterprise. This can directly innervate the management of the company and the various departments, such as:

  • Loyalty service, understanding the irritating factors that make customers flee and eliminate them; or converting neutral or dissatisfied customers into ambassadors
  • Customer service, understanding and eliminating the problems encountered by the customer during their journey, which are often a source of tension and directly impact the operational efficiency of processes such as customer satisfaction
  • Digital and e-commerce teams, by identifying real-time security, payment or promotional code specific issues to improve the online shopping experience
  • The marketing department, exploiting the gold mines contained in customer conversations to understand the products and trends that interest customers or the mindset of customers to ensure that the brand is well in line with expectations of its customers, and define targeted campaigns and the most effective sales processes
  • The sales department, which can benefit from the dashboards that can be used to remind customers who have abandoned their shopping cart due to an identified irritant or who are threatening to compete because of a malfunction on the customer journey

My belief is that today, customer knowledge (who, what, when and where?) Is not enough anymore. We must go beyond and take advantage of the data still poorly exploited in the voice of the customer. For this, artificial intelligence technologies, in particular,NLP and machine learning, increase the possibilities to provide brands with a real-time map of the experience that customers experience and improve it throughout the journey – in particular the failures and points of friction that encourage customers to leave and are expensive for companies. In this, customer intelligence represents a breakthrough innovation in the field of customer experience.

 

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