The analytical techniques that mark the beginning of the Digital Transformation and the good path on the road to the optimized customer experience that allows customer intelligence and Design of the value of the customer life cycle.
Combining historical and predictive models determine the current and potential lifetime value of customers, in order to understand where to allocate resources and forecast the highest value that could be achieved if customer relationships are optimized.
Table of Contents
1. Predictive Modelling
From the prevention of the risk of customer abandonment to the decision on the course of action to be taken at all times, this type of analytical techniques are widely used in organizations.
For this, multidimensional models have used that record the propensity of each client to purchase certain products, identification of the probability of response to different ones, and detection of the first symptoms of attrition that reveal those who have a higher risk of abandonment.
2. Customer Voice Analytics
Helps organizations listen, interpret, and act on what their customers are saying, reporting on what really matters or worries them. This feedback, based on structured and unstructured data, leaves the company in a position to act and drive change at different levels, with a sufficient margin of safety.
3. Customer Segmentation
The classification of customers and potential customers into groups that are determined based on similar characteristics, common interests or shared motivations, allows identifying and quantifying opportunities and risks across different segments, facilitating prioritization of strategies better aimed at success.
4. Multichannel Behaviour Analysis
Starting from the identification of channels and customer interactions in all of them, it is possible to collect data that inform the optimal way to manage resources through each channel, adjust spending and identify opportunities for up-sell and cross-sell.
In this sense, a good example is the SAS 360 Engage solution, which makes it possible to target and send offers very effectively to customers who have interacted with the organization through digital channels such as email, mobile applications or the web.
Challenges To Overcome To Reach The Level Of Customer Intelligence
The problem is not necessarily in the means and techniques used, but what makes it difficult to reach the stage of customer intelligence is the burden of having an outdated culture, strategies and processes. This inertia weighs and slows down the progress of organizations that resist change or encounter serious difficulties in initiating an appropriate Digital Transformation, especially if it is taken into account that the new scenario requires the effort to renew many of their capacities.
The most important challenges to consider are:
1. Insufficient Understanding Of Online Interactions With Customers
Not knowing exactly what happens when a customer interacts with the company through digital channels, prevents designing and applying a strategy that is truly customer-centric. You have to be able to travel to the centre of the data to extract all its value but to do this, you need a strategy that supports this line of action.
2. Lack Of Of Individualization Capabilities
Traditional solutions do not allow the selection of target customers in a cross-channel environment and this can lead to problems of customer abandonment and loss of customers to the competition.
You have to make a correct technological choice, but getting to this point requires a prior self-evaluation that allows you to identify the weaknesses and needs of the company.
3. Difficulty Accessing Knowledge About The Client
The reporting capabilities of traditional web analytics solutions are complex, technology focused, and primarily targeted at IT users and web analysts; making it difficult or impossible to obtain customer information in the hands of vendors, who are most in need.
It is necessary to ensure that the vision of the customer and the knowledge that the organization has about it is accessible and actionable at all levels, so the company culture must be worked to promote interest in understanding the customer and gaining insight about him.
4. Absence Of Uniqueness In The Client’s Vision
The difficulties many companies experience in accessing and sharing customer information across all channels results in incomplete customer insight and the inability of business units to fully understand and predict their behaviour.
We must bet on effective integration to improve personalization and avoid mistakes, and for this, the first step is to blur the barriers between IT and business and promote BI self-service.
5. Inability To Use Knowledge When Developing Ties And Strengthening Existing Ones
Collecting, standardizing and using digital data for marketing activities often takes weeks, a time when the chances of success of any business initiative are greatly reduced.
Real-time analytics must be applied to shorten cycles and achieving this depends, among other things, on a transformation of the business at the infrastructure and process level.
