Traditionally, analysts have always mined and studied historical data to diagnose the success and failure factors of businesses and to build best practices for future. In the case of failures, it is impossible to reverse the damage already done which can be at times fatal. However, the best practices did not involve a scientific or systemic planning for the future or to anticipate and be prepared for sudden changes. This leads to confusion on planning and budget allocation tasks. All future steps were based on intuitions, experience and at times random chance hence creating a high probability for errors. The primary reason for the system to not provide such opportunities was due to the lack of robust tools and understanding of concepts.
However, the scenario has changed a lot in the previous decade with revolutionary growth in data collection mechanisms and business intelligence platforms. In today’s competitive markets, small margins of error can lead to drastic changes in profits/revenue numbers, customer satisfaction index and even losing of customers. Also, the changes are so dynamic that it’s impossible to wait for the diagnosis of the past; real-time suggestions and customization have become necessary. The need of the hour is foreseeing, quick, efficient, actionable insights that are bang on and have a definitive business impact and this requires going a step further, identifying the underlying patterns and bridging the gap between the actual and expected outcomes. Advanced data modeling is a much more feasible task in today’s scenario.
Taking a random example consider an e-commerce website. A visitor comes to your website for the very first time; the visitor goes through 3 pages of the website and from the next pages product recommendations very specific to what the visitor is looking for, pops up. From Nabler’s experience we can guarantee that people LOVE customized recommendations and the probability of more people finding what they were looking for rises causing conversions to rise significantly. This kind of an outcome could only be dreamt of by using traditional techniques but made into reality by predictive techniques. So how was it possible to recognize what a customer wants from his very first visit? There is a model that runs at the background and for a first-time visitor matches the pattern of a visitor from historical records and using advanced statistical modeling pulls up the products that a visitor with such a navigation behavior has the highest probability of buying, is presented to the visitor.
To benefit from the advantages of predictive analytics, the brands need to create an environment for predictive analytics to thrive and provide valuable suggestions and strategies. The company must be in a position to assimilate, implement and take actions based on the insights. Brands need to explore ways to convert the suggestions to actionable solutions with minimum turnaround time, and constantly work towards reducing the turnaround time and make the implementation as real time as possible.
Predictive analytics requires specific ingredients to be optimally effective such as – clean and well-maintained data structure, documented definition and understanding of KPIs/metrics, error-free tracking and reporting of data, and understanding of the maturity level of the business. Strong understanding of the factors mentioned above removes possibilities of unrealistic expectations and non-implementable solutions.
Nabler team has helped many clients identify appropriate KPIs for the business through KPI workshops. Nabler digital consulting team can facilitate discussions with various business groups within the organization to understand their requirements and expectations with data and determine the KPIs accordingly. Nabler team can also work with the brands to identify suitable tracking tools and mechanisms through auditing and help with the implementation of the tools. It is also essential for the brand to understand the maturity level of the organization to decide on the focus for the future and enable predictive analytics to help set targets and devise strategies to achieve the same.
In this era of cut-throat competition, you need to understand customer segments and target them with customized packages, unearth what is causing your sales drop, quantify the sentimental value of your brand, which vendors are consistently under-performing how your customer executives are faring and so on, involving an ever improving cycle where with every learning the prediction gets closer to the realized outcome.
The time to implement Predictive techniques is here, you now have the opportunity rather than hitting in the dark, to make an educated guess in the right direction helping you to make smarter decision making and improved results giving you the competitive advantage before your competitors find it against you.