3 ways data quality impacts Predictive Analytics

The era of Big Data has given rise to a major misconception – the idea that data volume alone can provide marketers with a clear picture of an individual. That’s simply wrong. Having more data is great. But it isn’t everything. Here are three other factors you need to consider when using data to improve your marketing strategy. 

1. Level of detail:

Anytime you’re analyzing an event, you want all the details, along with the historical record of the individuals involved. The more data points you have, the better you can react, and the smarter your next action plan will be.

An attorney equipped with the full details regarding his/her client’s case will be more effective. A doctor who knows everything about his/her patient’s medical history, habits, and existing behaviors will be better equipped to aid the patient. It’s the same with your customers. The better you understand the customer journey, the easier it is envision every scenario and define a success story.

Comprehensive data capture is instrumental to every marketing strategy, as it directly impacts the accuracy of each prediction. The machine can only learn with the details we provide. It is agnostic to context, and will never ask for more details. That means it is incumbent on us, marketers, data scientists and analysts, to ensure all data is presented in the right way.

2. Connections within data:

Having first party customer data sitting in multiple silos does a business no good. Anything you track about customer touchpoints or their digital trail becomes a key data point worth analyzing for its correlation to the prediction outcome, so you can answer questions like:

  •  Which customer will buy if I run a promotional offer on this product?
  • Which of my customers are best and worth an additional discount?
  • Will this product sell?
  • What is the price point I can set for this NPI to meet demand without leaving money on the table?
  • Where is the likely geography this product category will sell best?

There is no limit to the questions you can ask and resolve using predictive analytics. But how do you decide which variables are worth factoring in?

             There are two parts to that question. First, we as humans decide what is deemed relevant to consider at a very high level. It is the data scientist’s role to study the outcome required for that industry and ensure the right factors are incorporated. Second, once the universe of relevant, comprehensive data is fed into the machine learning models, it can highlight which of those features have a strong correlation to the outcome in question.

Connecting the data across all systems is a key process for any business. For instance, if the ideal equation should be [a + b + c + d = outcome], yet you consider only one variable, prediction results will be skewed and misleading.  Those variables {a,b,c,d} could be set of signals, data points sitting in silos of your multiple disparate data systems.

Without connecting the dots between the data points, the outcomes you predict are sure to be inaccurate.

3. Completeness of data:

Let’s imagine you send multiple catalogs to your customers. Some result in sales. If you analyze only the sales transactions, how can you get deeper insight into the impact of sending that catalog?

A data point that delivers an average number of catalogs needed to convert a customer of your chosen profile would help you determine not only how many catalogs you need to send, but also where to send them. Predictive analytics isn’t just about determining whether the customer will buy based on their LTV or purchase history, it’s about knowing the function of your marketing strategy on sales outcomes.

At VectorScient, we have strong feelings on data quality: “Garbage in, garbage out.” If the data you feed into the machine is no good, the resulting predictions will be useless. That means marketers must focus on more than just data quantity—they also need to put data quality into perspective when eyeing for the right product for their business.

In summary, while it is not an easy target to connect all the relevant data across all the disparate data systems, it is important to acknowledge and recognize that it forms the strong foundation feeding directly into the accuracy of the prediction outcomes.

Thanks for your time and attention. Have you looked into all the 3 ways of comprehensive data coverage for your business? I’d love to hear your thoughts on this.

About the Author: Veda Konduru is the Founder and CEO of VectorScient. In her current role, Veda is responsible for crafting and executing VectorScient’s strategy. Veda also serves as Chief Product Architect for VectorScient’s Predictive Marketing Cloud. Prior to launching VectorScient, Veda spent more than 10 years in corporate world, architecting and building software applications for Sales, Demand Forecasting and Supply Chain, with reputation for excellence in product quality, Customer Focus and engagement. Veda blends her Statistics background with Machine Learning and Data Science to create truly world-class Predictive Analytics product. She holds Master’s degree in Computer Sciences.

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