The marketers of today know the importance of engaging consumers through personalized messaging with the right touch points. Still, it becomes difficult to cut through the noise of instant email and piles of direct mail. With companies locking in on the necessity of reaching their customers with as much personal detail as possible, how does a brand continue to stand out? That’s where the secret of data science comes in, and marketers are beginning to use this lucrative resource to restructure entire advertising and marketing campaigns.
Data science is a vastly growing field that continues to expand as trillions of bytes of consumer data are collected every day. At its core, data science takes numbers and gives them meaning. This meaning can predict buyer behavior, and in turn influence marketing campaigns and major business decisions to maximize return on investment. Digital Shuffle, CCG’s agency partner, has created a data science platform built on cloud-first native operations utilizing Python and enterprise business intelligence to benefit our CCG customers from the ground up. The beauty of this proprietary platform is that it uses your existing data.
Customer Analytics are our passion. Digital Shuffle uses three distinct behavioral models to help marketers make informed decisions about their marketing campaigns. These include customer churn, associate rules mining, and customer lifetime value. Digital Shuffle collects data from our clients existing customer relationship management (CRM) software and analyzes it to create the three behavioral models. The agency then puts the results into a digestible form for the client, replacing the need for them to have their own analyst.
By definition, customer churn is when a business loses a customer for any reason. Churn can be passive or active. An example of passive churn would be consumer turnover due to a membership expiring that the consumer never reactivated. Active churn would be if a consumer left one company for another because they liked a competitor’s product better. The churn behavioral model assigns a quantitative value to the likelihood of a consumer leaving the company. The results of this type of assessment allow the client to make big picture decisions based on numbers and facts rather than assumptions.
Associate rules mining is a model that predicts consumer behavior as it tracks consumer movements to forecast what a consumer will do next. The model can track purchasing habits of each individual consumer. For example, when a retailer collects purchasing data from consumers, the model can surmise that whenever a certain customer buys a shirt, they will also buy matching pants. With this information, the model establishes association rules which allow the company to tailor a campaign, incentive, or discount to encourage the consumer to continue this purchasing habit.
Finally, customer lifetime value assesses a consumers’ value to a company by ranking them based on the revenue a customer will generate for a business throughout the lifespan of their relationship. With these consumer segments created, a company can modify its marketing campaigns and offers based on a consumer’s ranked value. For example, a business could offer high value customers incentives such as referral codes and cashback offers. Or it could use loyalty programs to entice lower value customers, who are more likely to leave, to re-establish allegiance to the company.
Digital Shuffle recently completed an in-depth data science effort with a customer in the transportation industry. The customer operates in both B2B and B2C markets across the U.S., providing testing and certification that verifies the technical knowledge of service professionals in a variety of disciplines. With more than 50 different certification tests, millions of database records, and over a quarter million active professionals, the customer was keen to have Digital Shuffle dive deep into their customer analytics.
Although the customer actively communicates with their consumers via omni-channel communications, the concept of using artificial intelligence and proprietary algorithms to identify purchasing patterns and probabilities within their consumer data was very exciting. Digital Shuffle’s data science platform allows customers to identify and slice their consumer population based on location, age, education level, employer types, years of certification, number of certifications, purchase behavior, and more, to allow for ultra-personalized targeting in marketing efforts.
Digital Shuffle began by creating a customer churn model, specifically designed to mitigate customer churn occurring from non-recertification (a professional certification is valid for a period of 5 years, then a recertification test must be taken to keep certifications up to date). Digital Shuffle analyzed a data set comprised of 43,108 unique individuals that had pending certifications expiring in 2021. The model was able to predict that 12,435 of those individuals were in a “high churn” category, 25,168 were in the “moderate” risk of churn category, and 16,697 were in the “low-risk” category. Accordingly, Digital Shuffle created a series of multi-touch, multi-channel communications with varying incentives (relative to churn probability). The following explains the paths taken to market to each churn category:
Low Risk of Churn: Multi-touch email communications deployed over a 45-day period, focused on communicating when it was time for renewal, why renewal is important, and where to renew.
Moderate Risk of Churn: Multi-touch email and direct mail campaigns deployed over a 45-day period, focused on communicating that it was time for renewal, why renewal was important, a variable incentive to renew, and where to renew.
High Risk of Churn: Multi-touch email and direct mail campaigns deployed over a 30-day period, focused on communicating it was time for renewal, where to renew, why renewal is important, an increased variable incentive to renew, and why other similar professionals would choose to renew.
A parallel effort was executed to incorporate an associate rules mining model, also known as “cross-sell/up-sell”. Data science provides answers to the question, “When a customer previously purchased X, what are they most likely to do next, and when are they likely to do it?” Armed with this information, Digital Shuffle executed a series of omni-channel print/web/email campaigns to suggest subsequent certifications to take the next step on the professional’s career path. Leveraging the power of data science, Digital Shuffle created completely unique suggestions for over 200,000 individuals, all completely tailored to the unique purchasing habits of each individual.
Data science has completely changed the way Digital Shuffle’s customers identify, understand, and engage their past, current, and prospective consumers. The ability to leverage extremely complex algorithms and artificial intelligence to visualize and predict consumer behavior is revolutionary. The days of basic demographic, geographic, and psychographic data are truly in the rear-view mirror. Interested in learning more? Visit DigitalShuffle.com, or contact your CCG rep to schedule a discovery call.
By Marley Niesz
Do you remember the world-wide phenomenon and phone app Pokémon Go? It felt like all children and adults alike were spending the entire day walking around with their phones to catch Pokémon. Although the craze subsided, this app was one of the most widely known and utilized examples of augmented reality. Augmented reality is computer-generated technology that adds to the reality you see. In Pokémon Go, augmented reality is the visible Pokémon in your actual environment. Augmented reality is different from virtual reality, which is a completely computer-generated environment. Phones and tablets work well with augmented reality technology. Another example of AR that users may be familiar with is Vito Technology’s Star Walk app. This app enables users to point their phone camera to the sky and see the names of stars and planets reflected onto the image they see. Although AR may seem like a thing of the future, it is here right now and consumers have already shown their enthusiasm for it!
Today, more and more marketers are realizing that augmented reality is no longer reserved for tech companies and video games—it can be an incredibly engaging marketing tool as well. In print, there are a number of exciting ways that marketers can deploy AR to create a higher than average engagement rate. Take a direct mail package, for example. How exciting would it be to receive a postcard that you could actually interact with—virtually? Augmented reality allows companies to incorporate a barcode onto their print piece which can be scanned by the consumer and then interacted with through their phone. A good example would be a home décor company sending out a postcard advertising a new desk lamp. With augmented reality, consumers could scan the postcard barcode and then see the desk lamp in their office, viewed through their phone. Project Color, a recently released app by Home Depot, shows users what a paint color would look like on their own walls. This encourages the consumer to visualize and interact with the item in their own environment, which can lead to more purchases. The possibilities are endless!
A major advantage of augmented reality is the amount of time and money it can save companies, especially those in industrial manufacturing. Imagine how much money and physical labor it costs Lockheed Martin to store and ship its large planes for trade shows. With augmented reality, all of these costs are eliminated. Instead, consumers can scan a barcode at a tradeshow and see a life-sized plane right in front of them, including virtual information about it. In retail, this technology generates fewer item returns because customers are able to see the products in more detail before they actually make the purchase. This is particularly useful in the age of online retail when less people are seeing physical items in stores and more are ordering online. According to a study by Ventana Research, the three most important AR capabilities for consumers are getting detailed product information, reading product information, and accessing product information easily on mobile devices. Simply, augmented reality is an efficient way to market products while also increasing uniqueness and consumer engagement, not to mention its increasing importance during the pandemic.
The beauty of augmented reality is its versatility and ease of incorporation into existing marketing strategies. Although competitive disruptions are bound to intensify across industries with the increasing popularity of AR technology, it remains a smart investment that can generate higher ROI. Costs incurred by the implementation of AR technology are easily offset with the accompanying decrease in costs such as shipping and travel expenses and other supply chain costs. Additionally, AR can have a larger reach than traditional marketing touch points because of its virtual nature.
We’ve seen many great augmented reality marketing campaigns—and I highly recommend if happen upon one to experience it yourself. One of my favorites is from Absolut Vodka. The company placed neck ring tags around each bottle and when scanned the consumer could view and interact with a tour of the Swedish village that distills the vodka. Not only does this endear the consumer to the brand or product, it works to make the consumer feel connected to its history and story as well. Another great campaign came from The New Yorker Magazine in 2016. Illustrator Christoph Neimann designed the cover and brought it to life with the magic of augmented reality. When readers scanned the cover, it became three-dimensional and animated right before their eyes. Augmented reality is truly capable of turning a simple print campaign into a living work of art.
The importance of augmented reality in various industries, especially marketing, will continue to grow exponentially as consumers increasingly value convenience and rely on the online market for making purchases. Augmented reality fits seamlessly into traditional marketing techniques while presenting the opportunity to advance them to a highly interactive level. Backed by our creative agency Digital Shuffle, CCG frequently works with AR technology to enhance any print project. Let us know your needs, and we will bring our capabilities!
By Marley Niesz
Data mining is a huge buzz word in today’s digital world. Love it or hate it, the ability to mine data has truly changed many industries for the better—but at what cost? The question many experts and consumers are asking is—how far is too far when it comes to our privacy? Is it ethical or legal to buy and sell the personal and private details of a consumer’s digital footprint, lifestyle, etc? These are questions corporations and lawmakers face on a daily basis. But data mining isn’t going anywhere, and artificial intelligence continues to refine itself. So where did data mining begin, and where is it going?
Data mining is the process by which computers analyze large sets of data and use them to predict behaviors and future trends. Companies use models, such as a set of examples or mathematical relationships applied to different situations, to make these predictions. Through data mining, businesses can retain information about consumer behavior that could only otherwise be discovered and studied in focus groups, case studies, or other more time-consuming and expensive methods. It is efficient and accurate, giving corporations and industries access to information they never had before. Today’s data mining technology is highly advanced, but it wasn’t always as such.
Data mining has evolved every decade since the 1960s, starting with its conception of data collection. Data collection is self-explanatory, but it was initially enabled by computers, tapes, and disks, allowing businesses to retain information about simple concepts such as revenue totals or sales history. The 1980s saw the birth of data access, which enabled industries to collect more minute details about their day-to-day business dealings. This data could be stored and reviewed at another time through relational databases, which were also capable of establishing connections between data points. This led to the invention of data warehousing which reports and analyzes data. Data warehouses store current and historical data from multiple sources in one place and are used for creating analytical reports. Data mining was developed from all of these technological advances.
There are many specific uses of data mining for marketers. One is market segmentation, which groups consumers into segments based on common characteristics, allowing companies to target them in advertising campaigns. Similarly, direct marketing uses data mining to identify customers that will have the highest response rate probability to direct mail. Another is “customer churn” which predicts and identifies customers who are most likely to leave the brand for a competitor. For security purposes, data mining can be used as fraud protection to identify fraudulent transactions. Interactive marketing and market basket analysis predict individuals’ interests, future purchases, and products they are likely to buy together. Finally, trend analysis reveals the differences between typical customers from month-to-month. In general, marketers use data mining to predict consumer trends and behaviors and discover unknown patterns between consumers and transactions.
So how do companies access this data? Is there some sort of data black market where corporations put consumers’ profiles into a basket and check out? A data Amazon? Thankfully, for consumers like you and I, there is an entire professional (and not sketchy) industry focused on data mining. The professionals who work in this industry, buying and selling data, are called data brokers. Data brokers collect information from public records, online activity, and purchase history (to name a few) and then sell it to businesses who use it to influence marketing decisions. Some of the largest data brokerage companies store data for more than 500 million consumers all across the world. Data brokers also purchase data from specific companies who sell information, such as lists of consumers who belong to loyalty programs. This purchased data is then sold to other companies who may use it to make decisions about their own loyalty programs. The options, like the data, are endless. Unless a consumer is living entirely off the grid it is safe to assume a company owns their data. Luckily, some companies offer consumers opt-out options which prevent their personal data from being sold or rented.
Although the opt-out option leaves consumers with some control over their data, legislators across the United States are fighting for more consumer privacy rights. Recently in the news, Facebook made headlines after it was revealed that hackers sold the online identities of 267 million Facebook users for the price of $540. The data was comprised of users’ email addresses, names, Facebook IDs, dates of birth, and phone numbers. Although no passwords were stolen, users could easily fall victim to phishing and accidentally give away more serious private information. Data breaches and insider trading like this happens every day, and law makers are using these examples to lobby for their constituents.
With new technology, data mining has come a long way since the early days of data collection. As artificial intelligence becomes more prominent across all industries, data mining will continue to grow as a powerful tool for marketers and businesses alike. Although it comes at a price to consumer privacy, new legislation offers protection and a nice compromise between personal security and business efficiency. It will be interesting to see how this balance plays out between public and private interests. For more information on data mining and other direct marketing techniques, order Corporate Communications Group’s Direct Mail Marketers Guide.
By Marley Niesz
10 Ways To Use Case Studies in Your Marketing e-book. Fast read, great tips! Check it out today.
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