Data Science is Amazingly Revolutionizing Marketing

Data science and the availability of copious amounts of data on customers are profoundly bringing about an overwhelming transformation to marketing. Nobody talks about the Barter system again, where you exchange one product for another and all the stress involved in it.

Gone also are the days when marketers could attract customers only by taking out full-page advertisements in magazines, plastering the town with bright billboards, and even signing up glamorous celebrities for television commercials. Marketers have overly become more effective, extracting value from data by automating and optimizing processes or producing actionable insights.

Marketers have been able to do this by understanding the precise requirements of their customers from the existing data like the customer’s past browsing history, purchase history, age, and income. The interesting thing is that you probably had all these data earlier too, but now with the vast amount and variety of data, you can train models more effectively and recommend the product to your customers with more precision.

Wouldn’t it be amazing as it will bring more business to your organization and greatly increase your ROI? It was estimated that the application of data science could account for $300 billion to $450 billion in reduced health-care spending or 12 to 17 percent of the $2.6 trillion baselines in US health-care costs.

What you are now able to do as a marketer is that you can blend various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. To reap all the benefits of data science, however, you need to follow all the phases throughout the lifecycle to ensure the smooth functioning of the project.

Data science is responsible for mapping social networks and illustrating customer personas. It also identifies demographics and locations, in addition to tracking target audience responses and moods.

Data science has enabled brands to customize their customer experiences. It also helps develop new approaches to long-held marketing challenges while  insights are connected to marketing results.

What obtains is that data, statistical algorithm, and machine learning are used in Predictive analytics to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to provide the best assessment of what will happen in the future.

One of the most fundamental changes that have taken place in marketing has been the transformation of the idea of customer segmentation. We now live in the era of ‘segment of one’, where every customer is a segment unto himself or herself.

There is a much deeper, almost intimate, understanding of individuals that companies and brands target, enabling them to personalize their treatment of each customer. The first step in being able to do this effectively is to build an understanding of a representative customer lifecycle.

The customer lifecycle can be divided into 4 phases: Acquisition of customers, On-boarding of the new customer, Growth, and Fading & Attrition.

1. Customer acquisition

According to a survey conducted by Forbes Insights in association with Turn, it was reported that only 25 percent of marketers are collecting customers’ data. This is somehow absurd at a time when we live in the era of “segment of one.”

A recent study conducted by Forrester Consulting revealed that 64 percent of marketers believe that they need better data for prospecting, and 67 percent agreed that customer acquisition is more challenging than retention

Predictive analytics comb through consumer information to determine if a customer is likely to purchase a product or not, and can offer help when it comes to bolstering revenue. If you’re really committed to acquiring new customers, you should consider tapping into the power of contemporary data science to market to prospective shoppers on social media platforms.

You don’t, however, need to scoop every bit of data that is out there. Your data is only valuable insofar as you can use it, however, so keep your data collection efforts narrowly targeted towards those audiences most receptive towards your products and services.

The bottom line is to turn data into action.

2. On-boarding a new customer

As customer success thought leader Lincoln Murphy famously says, “the seeds of churn are planted early.” This collaborates the fact that we are in a world where 40-60% of software users will open an app once, and never log in again.

According to Sixteen Ventures, an onboard customer is two things: One that has experienced “initial success” with your product and one that sees the real value potential in their relationship with you. Groove HQ, from a slightly different angle, says that customers are onboard 1) the moment they sign up, and 2) the moment they see success with your product.

If the on-boarding is well-executed at every touchpoint from the first contact to purchase and thereafter, it can be the beginning of a long and fruitful relationship. That could range from client-only VIP portals, personal profiles, exclusive content and offers to simply communicate, being readily available, and understanding enough about your customer to know the questions they’re about to ask and answer them before they wonder.

According to Ricardo Craft, SVP of Solutions and Professional Services for ServiceSource, the following are five essential client onboarding best practice “must-dos:” Outline the actions required for your client to achieve business outcomes, map client onboarding milestones, share your client journey map and milestones, become data-driven to monitor key milestones, and proactively manage exceptions.

Bain & Company research showed that 5 percent increases in client retention can boost profits from 25 percent to a whopping 95 percent. This goes to show that adding new clients to the roster is a worthy pursuit, but retention should be given due consideration as a fundamental factor in overall profitability.

3. Growth

The challenge in this phase is about how to increase what the customer would likely spend on their own as he or she grows. Stockpiling more records isn’t the answer to better marketing growth; data science is.

To achieve this, marketers focus on optimizing their marketing campaigns such that for each individual customer they are able to hit all the right triggers. An analytical approach that can be used to effect this is cluster analysis.

Cluster analysis is also called segmentation analysis or taxonomy analysis.  More specifically, it tries to identify homogenous groups of cases if the grouping is not previously known.

A market researcher may conduct a survey covering the needs, attitudes, demographics, and behavior of customers.  The researcher then may use cluster analysis to identify homogenous groups of customers that have similar needs and attitudes and take actions based on where the individual customer is deviating from cluster behavior.

4. Fading & attrition

While the most fundamental marketing use cases are still about acquiring customers, increasing engagement, and preventing attrition, there are of course multiple other goals that marketers could fulfill by leveraging data science or AI-driven applications– sentiment analysis, product bundling, pricing optimization, loyalty analytics etc. being some examples.

What is often overlooked is the fact that the cost of reacquiring a lost customer is almost as high as that of getting a new one, while that of saving a fading customer is much lower. Early warning signals could manifest in several ways and the goal should ideally be to raise the red flags and intervene.

Data is the new oil, using it, in combination with robust data analytics backed strategy, presents extensive opportunities to catapult the efficacy of the marketing function within an organization.

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