Predictive Marketing: Here Is How Big Data Redefined Our Marketing Strategy?Salih SARIKAYABlockedUnblockFollowFollowingJun 17Data has been and will always be critical to every product marketing campaign, the nature of today’s marketing space crowns data as the indisputable king.
I have more than 10 years of media, product and marketing experience.
I have always used data before deciding what to do next.
But until a couple of years back it was solely based on the analytics tools on the market.
Then with the boom of data science, we have met with predictive marketing, enabling us not just with the past data but also future forecasts as well.
Photo Credit: Unsplash, by @ pietrozjA Definitive Guide to Predictive MarketingI have worked in traditional media as well but it is not news that digitalization has taken over a great deal of marketing, and now determines how we perceive brands and eventually make our decisions on new tools we use or purchases we make.
Globally, 1.
9 trillion people are expected to buy online.
While in the United States, 79 percent have already made an online purchase up from only 22% back in 2002.
What is exciting, however, is the level of sophistication that goes into the process of digital marketing to rein in better leads and increase chances of sales.
With several leaps of advancement in technology, marketing is becoming more and more sophisticated.
With the emerging companies solely based on online, digital marketing — the advertisement of products and services on the internet and electronic devices — has become crucial to businesses edging over traditional marketing in the process.
Just as digital marketing has become central to our business, market data has become an essential determinant of success in the marketing process.
Needless to say, that data has been and will always be critical to our every marketing campaign, the nature of today’s digital marketing space crowns data as the indisputable king.
We have used the data we obtained from several market sources to make informed marketing decisions such as how best to target adverts and the ideal marketing budget.
To improve marketing decisions, we have introduced more sophisticated ways to better harness data, analyze data, and utilize data in marketing to achieve better results.
We can capture one of these advancements under the term predictive marketing.
What is Predictive Marketing?Predictive marketing, as the name implies, is a marketing technique which determines the probability of success of different marketing strategies.
Beyond the buzzword, this is a subset of MarTech which is premised on the fact that marketing and sales are driven by data.
Therefore, in predictive marketing, we used data science (or data analytics) to predict which marketing actions are more likely to succeed and that which is more likely to fail.
We can name the part of data analytics involved as predictive analytics.
Predictive Analysis vs Predictive MarketingThough one could use both terms interchangeably, predictive marketing takes the predictive analysis of a business marketing a step further and has a broader implication.
While predictive analysis uses predictive models to provide insight into the future, predictive marketing uses predictive technology to test a business’ marketing strategy, provides insight and makes better marketing decision in a continuous (sometimes iterative) process.
In a simple scenario, a predictive marketing expert — usually a data scientist, data analyst or an analytics firm — gathers data about a business from several sources which it analyzes alongside the company’s marketing and customer data.
Armed with this information, the data scientist applies a predictive model suited to the business and predicts, with a level of accuracy, the success of its marketing efforts.
How Predictive Marketing WorksA classic case of predictive marketing at play is the e-commerce websites which recommend products and services to users based on their past behavior.
Right from the product search page to the checkout, those websites ensure that every returning user is bombarded with these product recommendations.
These recommendations, a product of the website’s “collaborative filtering” is based on the study of customer behavior like items the customer has in a shopping cart or items the customer or other customers have interacted with or purchased in the past.
The actual content of the algorithm is usually more sophisticated and may relate data with time, location, demographic distribution, and a whole lot of other metrics including open rate, click rate, and opt-out rates.
But it’s no longer just big companies can afford expensive in-house data scientists that can benefit from predictive marketing technology.
Our in-house predictive marketing specialists also gathered data from several sources and built predictive marketing models for our business.
They already had access to the company’s marketing and customer data as well as information on the marketing efforts.
The data analysts could then predict the success of the company’s marketing efforts.
Photo Credit: Unsplash, by @ arifrwThe Benefits of Predictive MarketingThe relevance of predictive marketing is very similar to that of data science in digital marketing.
First, predictive technology gives the marketer a better understanding of customer behaviors.
Slightly different from regular marketing data analysis, predictive marketing tools go ahead to proffer decisions without the data scientist having to interpret the data and make recommendations separately.
In other words, predictive marketing models say exactly what marketing strategy will likely work and which wouldn’t, thereby making it easier for a decision to be made.
Predictive marketing models can tell whether or not a customer will make a purchase, when and how they are likely to make the purchase as well as other business-specific predictions all based on data acquired around the customers.
By analyzing the customers’ previous behaviors, data vendors also can help companies decide on areas like marketing budget management, planning of market campaigns, lead generation, and conversion strategies.
Because it is based analysis of large and diverse amounts of data from the customers (as against gut feeling and guesswork), the decisions from predictive marketing are much targeted and produce better outcomes.
Furthermore, the predictive analysis took our company closer to automated marketing systems or what experts call prescriptive marketing.
At this level, marketing systems automatically analyze data and decide in real time.
Activities like model generation, lead scoring, and updating customer insights will then happen instantaneously.
At this level of proficiency, we were able to substantially improve customer engagement since we could easily segment customers, and deploy tailored marketing campaigns.
The result of this is obvious; got to optimize our marketing budgets, improve lead scoring, and increase revenue from sales.
Predictive analytics is the backbone of data-driven marketing and according to Jas Saran, Forbes, it can improve a company’s marketing efforts in at least six areas including, marketing mix modeling, upselling and cross-selling, web optimization, customer acquisition, profiling, and retention.
Here are some specific ways predictive marketing has benefitted our startup environment:How We Powered Customer Profiling, Customer Acquisition, and Customer Retention With Predictive MarketingSophisticated predictive marketing models help businesses create specific and unique customer identities based on their past behaviors and insight into their future behavior.
Because the data that is used to form predictive marketing models are often very detailed, marketing efforts built around them tend to have much higher conversion rates.
Customer acquisition efforts become more effective and efficient when marketing efforts are precise and targeted to just the right customers.
Again, thanks to predictive marketing models, the marketer can customize marketing campaigns and customer acquisition strategies to the different sections of a properly segmented potential customer base with a high probability of conversion.
Similarly, insights into the future behavior of customers help the business to plan its customer retention strategy.
When the company knows the segment of customers that are likely to leave, when they are likely to exit, and the conditions that make them go away, the company can design customized retention plans for each segment and do away with a blanket effort based on general assumptions.
When a business understands this, it can upsell and cross-sell to its existing customers with a higher probability of success.
Here is How We Improved E-Mail Marketing and Website Engagement With Predictive MarketingAccording to the Predictive Intelligence Benchmark Report, email marketing campaigns using predictive intelligence yield the highest influenced revenue.
Before predictive marketing, the marketer had to rely on broad generalizations (sometimes called personas) to create emails, a lot of which don’t get opened.
But predictive marketing has changed the game by empowering the marketer with specific information to create personalized emails which have better open and engagement rates.
Here’s an interesting stat: “Back-in-stock emails” and abandoned product email campaigns — which is possible through predictive marketing models — has the highest click-to-open rates (CTORs): 19% and 14%, respectively.
Similarly, websites that use predictive marketing models can improve engagement and sales by studying the behavior site visitors, especially around web assets like ad banners, product pages, and action buttons.
They can optimize the web experience with the information gleaned from visitors and customers.
In all, predictive marketing aims to predict conditions that will prompt a customer to purchase a product as well as help the business to optimize its pricing and other factors that influence the customers’ behavior.
In the case of Amazon, the company reportedly achieved about 30% increase in sales when the recommendations based on its predictive analysis model was introduced.
Photo Credit: Unsplash, by @ srd844How Predictive Marketing is Cutting B2B Marketing CostsBusiness-to-business (B2B) marketing is an area where predictive analysis is of great importance, especially in reducing marketing costs and increasing efficiency.
The cost of a single B2B could be anything from around $35 to $100 or more, it depends.
This high cost makes a low conversion rate really costly.
At $50 per lead, a 1% conversion would cost a B2B marketer $5,000 in lead generation alone.
It doesn’t come as a surprise, therefore, that a lot of businesses are jumping on predictive marketing solutions to better target their marketing efforts for a better ROI.
Here is an interesting stat: 98% of marketers who responded to Eversrting’s State of Predictive Marketing Survey that has at least a CRM, marketing automation, and a few marketing tools are either fully committed to or already implementing predictive marketing.
Predictive Marketing for B2B: Use cases and Best practicesFor B2B marketers, predictive marketing is becoming more or less an indispensable approach.
As we’ve seen, this approach saves the marketer some marketing costs, which is quite substantial for a B2B business.
In addition to this — and in line with the use cases of predictive marketing — predictive marketing affords the marketer some advantages.
Here are a few.
B2B Customer ProspectingFirst, B2B marketers can gain quality new leads by incorporating predictive marketing.
To do this, the marketer applies predictive marketing model through the company information and special signals to identify — with a good level of accuracy — businesses that are potential customers.
Information like the company size, products, and revenue when analyzed with signals like business expansion, management changes and thousands of other company data in a predictive marketing model can determine company behaviors that qualify as prospects.
Using predictive analysis models, the marketer can generate long lists of similar businesses with this behavior, which serves as a database of B2B opportunities.
Photo Credit: Unsplash, oowgnujB2B Lead Scoring (Lead Prioritization)Having obtained a list of prospective customers, the B2B marketer needs to decide which prospect is worth the first shot; that is the prospects which have a higher probability of becoming customers.
To do this, the marketer, once again, reaches for a predictive marketing model to prioritize the leads.
Lead scoring is vital as it informs the marketer on how to proceed with marketing efforts; which prospects should be pursued, when and how.
By using a predictive model, the B2B marketer improves upon the efficiency of manually-built lead scoring formulas.
The predictive model used could be based on the likelihood of purchase or lifetime revenue, profitability, promotion response, sales acceptance, or a mix of various factors.
This depends on the business goals and priorities.
B2B Lead SegmentationNext, the B2B marketer can proceed to divide the prospective customer into various segments, again with the aid of predictive marketing technology.
An ideal model will create personas of customers and divide the potential customers into groups according to their characteristics and behaviors.
These segments are based on personal interests, company characteristics, and interestingly, past behavior.
Because past customer behavior is a reliable and accurate basis for predicting future actions, lead segments based on predictive marketing models tend to provide a more personalized marketing approach, which in turn yields higher conversion rates.
Lead segmentation is vital to the whole B2B marketing process as it helps determine the exact approach for each segment instead of a generic marketing drive.
Apart from personas which are largely static, predictive marketing models allow the marketer to generate dynamic segments like “engagement level” which are often changing and tailor marketing approach per time.
Proactive Sales IntegrationSince predictive marketing models create dynamic customer segmentation, it empowers the sales teams with timely information (say prediction) about their prospects.
Coupled with quality lead prioritization, predictive models help make sale efforts more proactive, timely, and productive.
For instance, one model can predict windows when a customer segment is most likely to make a purchase and prompt sales team to intensify effort towards customers in that segment within that period.
Other information from predictive analysis, like the decision-making hierarchy based on past decisions, can help sales teams better coordinate their efforts and personalize their sales pitches.
Similarly, sales teams are better informed on when to make upsells and cross-sells.
Managing Full Life-Cycle of B2B CustomersApart from generating and prioritizing high-quality leads, predictive marketing technology helps the marketer deliver more significant impact across the customer life cycle.
These models support the marketer to engage the customer by providing timely information about the customer at every stage in the sales process.
Some Common B2B Predictive Marketing ModelsActual predictive marketing models usually fall into three categories namely; the segmentation models which focuses on grouping leads, the propensity models which tracks the probability of gaining or churning customers and the intelligent recommendations which predict which customers are candidates for more marketing efforts — usually an upselling or cross-selling.
Whereas segmentation models group leads or customers based on products (product-based clusters), and behavior (behavioral clusters), propensity models seek to establish ways of maintaining and expanding the customer base by pointing red flags and opportunities based on factors like customer wallet, competition, patterns from other customers.
Intelligent models — a precursor to prescriptive marketing — identifies areas to engage the existing customers better.
Data Vendors and SaaS Providers for Predictive MarketingAlthough services related to data science seem expensive, they are more within reach of regular businesses, unlike the early days when only big companies can afford to hire teams of data scientists.
Thanks to a growing list of data vendors and Software-as-a-Service (SaaS) systems that provide cloud-based predictive analysis.
Here are some leading data vendors in the predictive marketing space including Everstring, Infer, Mintigo, SalesPredict, Floptop, 6sense, and Lattice Engine.
Everstring boasts a robust predictive marketing engine and a full-featured data management platform.
Since came live in 2012 it has served a number of high-profile clients including ComcastInfer specializes in fit scoring, which predicts which future lead is ready to buy and then creates a database of leads.
Sales and marketing software company HubSpot uses Infer’s fit modeling.
6sense, which serves Dell and Lenovo is one of the later entrants in the predictive market space.
It emphasizes its ability to predict the intention and decision making of customers, otherwise known as intent scoring.
LeadSpace is a social media data vendor with integrates predictive marketing insights to improve the quality of net new leads.
Microsoft and Adobe use LeadSpace.
Photo Credit: Unsplash, frankiPredictive Marketing is the Future of B2B MarketingAfter getting the quality results from campaigns, anyone can easily suggest that the future of marketing is automation, and at the center of this is predictive marketing.
B2B marketers are increasingly adopting predictive marketing according to Everstring & Forrester report.
Here are some of the key findings:• A whopping 2.
9x the number of Predictive Marketers (41%) reported revenue growth much higher than the industry average compared to 14% of retrospective marketers that reported this positive business result.
• Half of the Predictive Marketers claim a commanding leadership position across their product and service categories, compared with only 24% of retrospective marketers• 49% of Predictive Marketers said their organizations consistently exceed company benchmarks compared to only 28% of Retrospective Marketers that were effective at delivering similar value to their business.
On these three fronts, predictive marketing — and data analytics-driven marketing in general — is setting the pace for B2B marketers.
According to Salesforce, 91 percent of top marketers are already implementing predictive marketing.
It is also attracting attention from business financiers.
Venture capitalists and industry players have invested over $5 billion in predictive marketing and similar data-driven marketing in only a few years.
Predictive Intelligence Benchmark Report also shows predictive intelligence recommendations influenced 26.
34% of total orders.
When analyzed over a period of 36 months, the total orders influenced increased from 11.
47% to 34.
71%.
Challenges of Predictive MarketingI absolutely have some concerns about predictive marketing taking over the human contribution.
A major challenge to predictive marketing technology lies with the removal of “human” contribution as the system heads towards fully-automated marketing analytics.
Some experts agree that humans still have some advantages and the increased limit of the human contribution through automated predictive systems could negatively impact sales.
As an emerging technology, adoption concerns will always prop up especially as data analytics is expensive.
However, with the growth of scalable SaaS solutions which businesses can easily plug in is helping B2B marketers benefit from predictive marketing insights without breaking the bank.
Finally, concerns with data science like data quality, data volumes, data relevance, and privacy issues also affect the predictive analysis.
Photo Credit: Unsplash, sortinoConclusionAfter adopting predictive marketing, our business both at Smartereum and WhatsAround reaped the benefits in terms of increased traffic, leads and revenues.
We were able to support our sales team by grading the leads automatically and give our focus on the best performing results.
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