Application of Predictive Analytics in the Marketing Industry

Data is growing at an amazing rate day by day. The data is generated everyday across various channels like social media, retail shopping, online shopping, blogging, video sites, search engines, etc. As this data grows at a staggering rate, companies have started using technologies to harness this data and derive meaning and insights out of it. Once such emerging technology is predictive analytics.

Predictive Analytics: Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. .(www.webopedia.com/TERM/P/predictive_analytics.html)  

Predictive analytics uses a combination of techniques which includes data mining, statistical analysis and game theory to provide predictions. Predictive Analytics has application in a lot of sphere which includes science and research, healthcare, marketing, retail, finance, operations, crime and fraud detection etc.  Many companies across the industries have started using predictive analytics to detect trends and patterns in collected data for exploring market opportunities, risk evaluation, cost minimization, capacity planning, etc. As these decisions are data-driven, the outcomes have proven to be far better than the original way of thinking. Companies are exploiting data using sophisticated predictive modelling technologies for gaining business advantage and competitive edge. 

The following graph depicts the usage of predictive analytics across various avenues:

The scope of this paper is limited to application of predictive analytics in the marketing industry and applications across the marketing industry.

Customer Analytics: 

 Marketing companies have been using customer analytics to estimate consumer behavior. This helps them to retain customers, gain additional customers. Using customer analytics, companies can understand behavioral patterns of the customer and impart more value to them.  Let us look an example for this. Retailing chains integrate their billing POS to customer databases to gather information about customers. This data can be used to detect buyer behavior and their purchase patterns. Using predictive analytics, the company can estimate the likelihood of a willingness or neediness of a customer to buy a particular product in future. The companies then can use this information to market this product to the customer through various media like promotional messages, mails or telemarketing. The companies can increase their sales by upselling and cross-selling additional products associated with the original product. This not only helps companies to increase sales but also helps in other areas as estimating demands, inventory management, operation management, production planning, etc. Using predictive analytics, companies can boost their revenue and decrease costs, thus substantially increasing profit margins. Thus, predictive analytics assists the companies to increase value imparted to the customers and increase their revenue and market share.

One of the best value perfective analytics imparts the companies is churn prevention. It costs less for a company to retain a customer rather than bring in a new customer. Using predictive analytics the companies can gain insight about what is causing dissatisfaction among a customer and make the changes or take additional steps to keep the customers satisfied.

Customer Segmentation:

 It is very important for companies to retain existing customer and acquire new customers day by day. Predictive analytics can identify buying behavior among various customer and utilize this data with other customer information like demographics, etc. to segment the customers into different groups. Hence, predictive analytics can do customer groupings and inform us with the insights of those groups. Companies use this information to market this segments differently and provide customer value. This information can be used to design different campaigns to increase reachability among different customer segments. Other additional feature is that the companies can focus on a particular segment and apply the insights uncovered through analytical tools and increase their sales on that particular segment. Thus, companies can have more focused and effective marketing campaigns. 

The chart below depicts the change in response percentage in campaigns when predictive analytics is utilized.

B2B Marketing:

The application of predictive analytics is not restricted only to the consumer market or the B2C market. It has a huge field of application across the B2B market as well. Predictive analytics is used by a lot of B2B marketers to identify and target new markets.  However, the usage of predictive analytics across the B2B is still at a very early stage.

Now-a day, the sales process of B2B markets have transformed from physical meetings to online meetings and purchases. These change in pattern of sales is generating a lot of data. Additionally, predictive analytics utilizes data from CRMs to do business forecasting.  A typical B2B company approach and estimate their sales pipeline based on likelihood or probability.    eg. Around a certain percentage of customers will respond to campaign among which certain prospects will be converted into leads and certain fraction of which will be converted into actual customers. Predictive analytics can be used to more accurately approximate this forecasts which in turn helps in sales planning and resource allocation in the company. Hence, companies use predictive analytics at the start of lead scoring and building ideal customer profile.

B2B marketers do a lot of marketing campaigns and use marketing metrics to measure the effectiveness of their campaigns. Predictive analytics uses the information gathered through marketing metrics tools and forecast effectiveness of future campaigns. This helps companies not only to make campaign more effective and focused but also save a lot of cost to the company. Experts agree that the early adopters of predictive analytics will gain a huge competitive edge across the industry and the firms that sit idle will be facing huge loss of opportunities and end up in disadvantage.