Being a marketer, one would recognize the immense power of data. Never before have we had access to data like we do today.

For many organizations difficulties arise in collecting, integrating and storing the data. However, making use of this data to drive better business decisions gives organizations a competitive advantage.

And I sure am not talking about reporting here.

Of course it’s intriguing to know what happened in the past and those monthly excel sheets might even get read once, but the organizations that use this historical data to focus on the future and predict future outcomes are the organizations that are surging ahead by leaps and bounds and are discovering enormous value.

When you look at the world of data science today, there is a lot of sophisticated work happening in the field that may be beyond your scope of understanding.

But, Predictive Analytics is something that is within reach for just about anyone and is waiting for it’s advantages to be exploited.

To put it simply, predictive analytics is making use of historical data to predict the likelihood of future outcomes.

The major case in point is increasing your measure of success because you can optimize anything that can be measured or defined.

Predictive models are very different from descriptive models – which can tell you what happened in the past, and diagnostic models – models that can explain or provide rationale as to why something happened.

Now that you know what Predictive Analytics is about, you should be intrigued about it’s applications.

We’re going to see 5 applications that will get you thinking about how you are going to make use of data to boost performance across various verticals in your organization.

Conversions – Yes, we’re all chasing conversions. At the same time it is critical to know who is converting and this is exactly where understanding and targeting the right prospects comes in to play. With the wealth of customer data already in your possession, predictive analytics can help you with quite a few things.

• Customer Loyalty : Predictive models will help you understand what segments and behaviors point towards the tendency to keep on consuming your products and services. Predictive models also help you understand the behaviors and attributes that are likely to cause a switch to another brand.
• Lifetime Value : As you’re scouting for new prospects and evaluating the existing customer base, you can make use of your data to forecast the net profit that will be accredited to the entire future relationship. How this helps is you can target your outreach, marketing campaigns, bonus/loyalty programs etc. more accordingly.
• Churn : Losing customers is never good for business. However, predicting the risk of a customer abandoning your brand can help you drive more targeted and personalized retention programs.
• Market Basket : The checkout basket can be turned into an advantage with the use of predictive analytics. You can understand which products are purchased together and which are likely to be purchased one after the other. This helps you identify your buyer’s purchasing behaviors.

Marketing budgets are better allocated when predictive analytics is used. The newest tools in the market, the best techniques when combined with the bundle of data being generated via every click and impression is a huge opportunity to make sure every marketing dollar is well spent.

• Marketing/Media Mix : There are lots of channels, up and down the funnel where you are likely to spend money. Being able to credit each touchpoint with value in the purchase path and predicting the budget allocation can help you attain more performance out of less spend.
• Audience Targeting : The “spray and pray” targeting tactic has become old school as today, we are gaining more and more data about who may become a customer and where we can find them. Predicting the probability of someone in the audience converting to a customer and the value that they bring can help the targeting become more precise and lessen the marketing dollars being spent.
• Purchase Intent : Usage of customer data/behavioral data to predict the intent of purchase for any lead/prospect can be immensely valuable to an organization. This can also be modeled to predict digital’s role in driving offline sales.

If you are investing in digital assets like websites and mobile apps, it only make sense that you’ll want to make sure that you’re getting the most from them.

Predictive Analytics can help you understand what factors will result in the best content, what areas can be customized to particular users and which areas of the digital experience are ideal for optimization.

• Content optimization : Time and resources are spent on creation, development and maintenance of content and it we have a lot of data about how the content is performing. From this data, pulling out factors that have been successful will help guide your content strategy in a way where you will produce pages and experiences with a high likelihood of achieving the set goals.
• Personalization : The combination of digital experiences and customer data results in you starting to segment and predict which group of users is likely or not likely to respond to your messages, offers etc. Today, the personalization tools give you the power to achieve user level customizations to give people what you know they are likely to want.
• Testing Strategy : A/B and multivariate testing is not a new phenomenon but the difficult part of testing is figuring out what to test. Predictive analytics can help you understand which grey areas of the experience need maximum improvement and it also helps define a hypothesis. Apart from providing a better experience for the users, the results can also feed the model for improved accuracy.

Risk is a very broad category. In reality though, all organizations try to mitigate risk with every action of theirs.

Data is used to pin point the factors that tend to create risk and then predict unwanted scenarios that are likely to occur in order for you to come to terms with the unknown and mitigate consequences.

• Fraud : This one is for the e commerce space where a lot of work has gone in. Organizations can use their own data in order to evaluate factors that are likely to be associated with fraudulent activities and in addition they can address these issues by improving security by adding more steps for checkout, selective payment options etc.
• Collection & Recovery : The accounts receivable has a direct impact on your cash flows and making sure you have a handle on accounts receivable is imperative as it also affects the organization’s ability to operate. Predictive analytics can help identify at risk accounts and will help formulate strategies that mitigate collections risk and have high success rates.
• Pricing : Pushing a product out in the market is influenced by price. With a price too high, there is the risk of acceptance and volumes ; with the price too low, profitability becomes an issue. Prediction of price elasticity, pricing gaps, thresholds and profitability targets can be done with the help of existing products and competitive data. This will help you arrive at an optimal price point.

Marketing and customers are extremely important, yes. However, at the end of the day the products and services have to be delivered with maximum operational efficiency.

Demand prediction to Supply chain management – Predictive analytics can prove to be an integral part of the planning and execution stages of operations.

• Forecasting : Be it planning of production cycles, demand predicition for new products and services or estimating financial performance, historical data can be used to model plausible scenarios or outcomes. Those models can be manipulated to understand what should be done now to impact the results you are most likely to see in the future.
• Network Optimization : Networks can mean many things, this may include supply chains, processes and just about anything that has inputs, outputs and dependencies. Using the data to work around the factors that influence the efficiency of each node within the process will help find the optimal paths through them.

These are just a few areas in which organizations can leverage the power of predictive analytics to make informed decisions about future states.

The tools and technology available today make these analyses accessible to almost every organization.

What’s left to do? Identify a business challenge, evaluate the data you have to work with and finally come up with a modeling solution that will help you see the future and make decisions driven by insight.

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