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In a competitive world where everyone looks to deliver the right offer to the right audience at the right time, it is important for brands to create a data advantage that will distinguish them from the competition.
One way to do this is to develop a data ecosystem -- a collection of infrastructure, analytics and applications used to capture and analyze data. In practical terms, it means mining data from prospective or existing customer emails, texts, social media posts and other sources. Those are the precious clicks that allow you to provide real-time, targeted offers based on the customer history while protecting customer privacy.
Creating competitive advantages with data collection
New and rapidly changing technologies make harvesting data related to customer activity more efficient, with a greater ability to target responses. The key is capturing and analyzing that data.
Why didn't that last text or email blast work as well as some thought it would? If it didn't get results, was it targeted to the wrong demographic? If it did work, why? What was the magic word or product(s) that made the reader click through? What does the data reveal as the reader clicks through several different levels?
Many of the big players in data collection have successfully learned to interpret data received from prospective and existing customers, and use that information to identify response patterns and develop complex predictive models. Building brand loyalty and delivering the right offer to the right audience at the right time is always the goal.
Special offers based on data analysis
The data received from digital customers -- or prospective ones -- can be analyzed to create unique offers that would appeal to specific audiences. Respond in real time without significant delay by targeting audiences on their mobile devices. This also addresses shoppers' short attention spans.
Complex predictive models derived can help grow the customer base while retaining existing clients who already look forward to receiving emails from the organization. Most customers appreciate the targeted approach they receive from smart companies that appear to have done their homework and are not wasting their time.
Know where to place offers
What may not work as an email may be a home run on social media channels. A lack of response to an offer made on one platform may be countered by strong interest and numerous click-throughs on another.
The response from multiple channels is funneled back to the sender and saved as part of the data ecosystem. Capturing that data and making it readily available is the crucial first step before any real-time analysis and rapid offer turnaround can be enacted. Think of that ecosystem as a data hub or data warehouse used to craft the next best possible offer.
More than half of businesses in the U.S. currently rely on prospective customer response for growth and retention but still struggle with data collection and analysis. They are good examples of how a well-refined data ecosystem can be used to develop predictive models, intuiting what products consumers will be willing to purchase -- and at what time of the day or week.
Future decisions are made on how customers react to current offers, again by analyzing their digital responses. It's not enough to just build that ecosystem. Success comes from knowing how to quickly transform it into an effective action plan.
Establishing a data ecosystem in which information flows from capture to analysis to actions taken can establish a competitive advantage. Some may choose to stay with more traditional marketing methods, but those companies willing to invest in data ecosystems will reap the rewards in a world turning more digital and mobile all the time.
Major players, such as Amazon, create synergy from customer churn; when shoppers purchase or browse through several items online, other items may appear for consideration, and customer engagement is key to this. Amazon is also able to fuel customer demand through special short-term events such as Prime Week, with concentrated sale items chosen in large part by predictive analysis.
Offers not presented at the right time can lead to market share loss, however. AI that enables systems to learn from previous interactions and make informed decisions based on data without explicit programming instructions is essential to a data ecosystem's ability to make special offers on the fly.
Security and protection
With more data available on a regular basis, businesses must invest the time and money to protect consumer information collected from third parties. Data security and a reputation for having that in place will lead to more confidence in email, text or social media recipients. Increased confidence can lead to greater trust and willingness to take that next step by clicking on a link.
On the other hand, customer data shared with another party -- especially without consent from the customer or stolen by a hacker and misused -- can have a disastrous effect on an organization and can lead to a loss of credibility that is never fully recovered.
Privacy regulations at the federal and state levels have been tweaking privacy laws on what type of information can be shared by marketing companies for years. Uniform compliance guidelines across the board are not in place yet, so it is up to companies collecting and using data to establish a high standard of ethics as they build their own unique ecosystems.
Beyond the numbers
Data can determine complex predictive models, which are a crucial factor for brands delivering personalized offers, building relationships and creating loyalty. These can include complex models targeting growth, churn, customer retention and more.
Data is the fuel to create a brighter future for business. Does it work? Look no further than industry giants such as Amazon and Walmart, that have reaped the benefits of establishing robust data ecosystems, while making the shopping experience more enjoyable for many consumers.
About the Author
Sankul Seth is the vice president of enterprise data at PSCU, where he oversees data innovation strategy. His current focus is on transforming technologies to simplify and define technology roadmaps and implementation. He holds an MBA in Data and Analytics from the University of South Florida.