Big data is big news. Industry-related buzzwords like IoT, data analytics, cognitive search, and machine learning are capturing the attention of leaders in every industry.
Once a business leader dives in and begins the research process, their attention is quickly drawn to another topic: Data monetization. There are many tools and resources available to businesses that wish to use the power of data to grow their business, and focusing on predictive analytics is one of the best ways that organizations can use data monetization to gain an edge over their competitors.
Gartner defines predictive analytics as an approach to data mining with four main attributes:
- Emphasis on prediction
- Rapid analysis
- Business relevance
- Ease of use
Predictive Analytics Key Terms
When discussing predictive analytics for monetizing data, here are some of the key terms and phrases you can encounter:
Analytical CRM (aCRM): Supports decision-making processes that improve customer interactions or increase the value of customer interactions; aCRM aims at storing, analyzing and applying the knowledge about customers and about ways to approach them effectively.
Churn Analysis (Attrition Analysis): Profiles the customers who are likely to stop using the company’s services or products and identifies those whose churn is likely to bring the biggest loss. Results of churn analysis are used to prepare new offers for valuable customers under the risk of defection.
Conjoint (Trade-off) Analysis: Allows comparing different variants of a given offer based on their utility to customers. It forecasts the likely acceptance of a product/service if brought to the market, can be used for product line management, price setting, etc.
Cross / Upselling: A marketing notion of selling complementary (cross-selling) or additional (up-selling) products to specific customers considering their characteristics and past behavior.
Customer Segmentation & Profiling: Grouping of customers with similar profiles and behavior based on the available customer data, describing and comparing such groups.
LTV (Lifetime Value) of a customer: The anticipated discounted profit that a customer will generate for a company during his/her lifetime.
Market Basket Analysis: Identifying combinations of products or services that frequently co-occur in transactions, for example products that are often purchased together. Results of such analysis are used to recommend additional purchases, inform decisions on placing products in relation to one another etc.
Survival Analysis: Estimates the time a customer will subscribe to a service or the probability of a customer’s defection in subsequent periods of time. This information allows the company to determine the predicted period of retaining the customer and introduce an appropriate loyalty policy.
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Some industries that currently use predictive analytics across a variety of applications:
- Life Sciences
- Oil and Gas
Let’s explore exactly how big data and predictive analytics can be utilized in some of these sectors.
Customers have a shelf life (or Lifetime Value), but when a business loses customers before that date has been reached, the loss in revenue must be replaced. Acquiring new customers is normally more expensive than retaining existing customers. Predictive analytics can help prevent churn in your existing customer base. It can identify signals in your datasets that or segment customers that are expressing signs of terminating a contract or service. Using this data, a company can reach out to dissatisfied customers to ensure they are kept happy.
For companies with infrastructure and equipment to maintain, being able to manage the initial expenditure on said infrastructure is important. Components typically have a shelf-life and being able to analyze shelf-life data associated with components can drive efficiencies. Predictive analytics can identify timelines and more importantly, cost associations for upcoming purchases for replacement components thereby allowing a business to streamline maintenance costs.
Social media has exploded in the past 10 years, torrents of user-generated data are created every second. Quite often, users are discussing products, business or services.
Businesses can listen to traffic across social channels and monitor their online reputation, identify new customers and enhance their web presence. Predictive analytics can leverage this data and feed it into models to arrive at future predictions for new products or service launches.
Customers are the lifeblood of any business and leveraging an existing customer is a valuable strategy. As they browse online stores, adding products to their shopping carts or viewing products, predictive analytics can help you track and segment related products. Related products can then be targeted to specific demographics thereby increasing revenue from existing customers.
Social media giants Twitter and Facebook allow you to implement and embed “tracking pixels” on your website. Tracking pixels are a way to allow advertisers to track user conversions and tie them back to original ad campaigns.
Companies can audit potential customers who have already shown an interest in a product or service that has arrived at their website via the tracking pixel.
This information can be run through a predictive model that can identify customers to be targeted on social channels with digital ads that are more likely to result in a sales conversion.
Attitudes to privacy have changed in the last 10 years; consumers will gladly share their data for convenience or to get access to a new online service. Companies are recognizing this and can better understand consumer behavior by leveraging this data.
By using products such as Tableau, marketers can visualize trends in overtime periods, identify insights or common search phrases/keywords. All of this allows them to make decisions easier in and stay ahead of the competition.
Historical data is often a good predictor of what might happen in the future. Imagine for example you are marketing a new beer and you that when the weather is good, you sell 20% more beer. Predictive analytics can leverage weather reports and stock levels to notify you when you should activate digital ads.
By auditing a user’s website journey through a company’s eCommerce store, businesses can pinpoint the paths that typically lead to an “abandoned cart.”
Why might this be important?
By using predictive analytics, one approach can be to dynamically tweak prices in real-time if a user reaches a product page via a specific “low conversion rate” path. Amazon implemented this approach to maximize customer conversion rates thereby increasing profits.
In this post, we’ve introduced your organization to predictive analytics, and you will now be able to discuss how it can benefit your business as a data monetization tool.
Here at Growth Acceleration Partners, we have extensive expertise in the analytics field. We can offer you Data Science as a Service. Our Centers of Engineering Excellence (COEs) in Latin America focus on combining business acumen with top-notch service to help your business.
We can provide your organization with a team of highly qualified data scientists and engineers who have expertise in:
- artificial intelligence
- data analytics
- data science
- information systems
- machine learning
- predictive modeling