Customer expectations and requirements have evolved over the years. Needless to say, customers detest being treated as the revenue generators and it is actually important for the enterprises to take a closer look at the satisfying metrics. Based on a study conducted by Experian Marketing Services in 2012, almost 84 percent of the existing customers snapped their ties with companies that fail to consider individual needs. Moreover, in this highly competitive arena we cannot afford to slack in terms of customer satisfaction as someone else will surely grab this opportunity and climb up the ladder.
That said, customer experience is a metric that actually determines the growth or fall of the concerned business. It isn’t a secret that companies are always on the lookout for successful user experiences— courtesy innovative services. However, it all boils down to data collection and analysis for brands that strive for increased customer loyalty. This is where Predictive Analysis comes into the mix, allowing enterprises take better decisions by comprehending the historical data sets and the futuristic tends. Leveraging analytics is a great way of gauging customer behavior and modifying internal strategies, accordingly. Obviously, it’s all about creating a personal rapport with the customers and predictive analytics can surely be a handful.
Defining ‘Predictive Analytics’
Predictive Analytics is a coherent entrepreneurial tool that involves statistical algorithms, machine learning approaches and data usage— all focused at identifying future outcomes. When it comes to the marketing scenario, this tool can also help companies determine the simplest and the most rewarding methods for upgrading the sales funnel by reaching out to customers at every possible junction. Predictive Analytics, if used optimally, can improve the overall conversion and retention rates.
Although, this approach has many upsides, it can be specifically leveraged for reverse engineering the past-experiences of customers. This strategy helps enterprises develop the marketing tools and resources which would make sense, even in 2017. It is therefore advisable to integrate Predictive Analytics with every stage of a given sales process. Be it working with a lead or converting the same into a prospect and then into a loyal customer— analytics can surely invigorate a company’s journey in the best possible manner.
Why Customer Experience Even Matters?
Ever wondered how it feels when customers keep posting negative reviews on the company’s social pages and the prospective leads change their minds upon seeing the same. Neglected customers can easily take down a business and this is why customer experience should always be held in high regard. While it is essential to collect historical data, enterprises can only make use of analytics if the existing data sets are aggregated in a right manner and timely fashion. Frankly speaking, customer experience is a great revenue generator and analytics help businesses understand the same, in the simplest possible manner. Be it the conducted researches, devised strategies and acquired insights— every marketing move aims at enriching the user experience. That said, brands that value the customers are more likely to outperform the competition.
Being Proactive with Predictive Analytics
Although analytics consider the past experiences, they usually thrive on the proactivity of the concerned organization. Any acquired data set should be used for predicting customer responses by identifying challenges and opportunities. This is where being proactive helps the marketers as Predictive Analytics basically aims at anticipating things— much before the occurrence. Then again, these are data-centric predictions with statistical validations and are expected to drive companies in the right direction.
The Starting Point
For the early adopters, Predictive Analytics can be slightly intimidating to implement. However, the best approach towards integrating the same involves defining company objectives. At the start, enterprises should ascertain data requirements and the company vision before moving ahead with a full-blown analytics campaign. Once relatable data is collected, companies should look at the relevant stakeholders, existing systems and other available resources. Considering each and every business module during a Predictive Analytics campaign makes it easier for the executives to work alongside the data-driven units.
Audience Segmentation is of Paramount Importance
Traditionally, segmentation happens to be an important consideration for companies that aim at leveraging customer data for better levels of satisfaction. Although Predictive Analytics helps improve the customer experience, it treads on the lines of in-depth customer segmentation. Analytics gradually unearth the hidden organizational patterns, thereby assisting firms with the advanced segmentation plans. Apart from that, this business tool also minimizes attrition and improves the efficacy of key touch-points for accelerating issue resolutions. Lastly, a well-defined and researched Predictive Analytics program also improves and boosts the cross-sell and upsell rates via sophisticated segmentation plans.
Predictive Analytics: The Impact of Suggestion Engines
One way of looking at Predictive Analytics is to gauge the modus operandi of various suggestion engines synonymous to the ecommerce websites. The likes of Amazon usually display products based on previous purchases and customer viewing patterns. With a powerful suggestion engine at the helm, Amazon has actually seen a massive 29 percent uptick between 2011 and 2012— in terms of sales figures. Not just retail chains but Netflix, YouTube and Spotify also have exceptional analytics-centric algorithms in place for enriching the customer experience.
Lately, companies have been finding it hard to decipher the customer experience puzzle and this is where analytics can surely come in handy. In the absence of Predictive Analytics, it becomes exceedingly difficult for businesses to process and analyze the massive chunks of data. However, it is all about acquiring actionable insights via reliable data sources which then can be comprehended for analyzing customer behavior.