• Restaurant Marketing

4 Retail Marketing Analytics Trends Which Will Dominate in 2018

by: Robin Johnston
3 min read

A robust analytics program is central to the success of your business.  Analytics will help you generate more leads, increase sales, gain insights into customer behavior and preferences, create effective customer profiles, measure return on investment (ROI) and maximize limited marketing dollars. 

Surprisingly, though, most businesses are still in the dark when it comes to the use of analytics.  For example, a recent survey from Harvard Business Review found that “more than 80% of respondents were dissatisfied with their ability to measure marketing ROI.”

What Is Marketing Analytics?

Every business is different, which means analytics will mean something slightly different to each of them.  That said, WordStream provides a useful definition:

“Marketing analytics is the practice of measuring, managing and analyzing marketing performance to maximize its effectiveness and optimize return on investment (ROI). Understanding marketing analytics allows marketers to be more efficient at their jobs and minimize wasted web marketing dollars.”

Analytics Is Growing—and Changing

Like everything else in marketing, best practice for effectively leveraging analytics is changing, in part because of new technologies and new data sources.  Innovative marketers are experimenting with multiple new approaches, but 4 trends are most likely to be dominant in 2018:

1.  Getting into the Weeds with New Data Sources

Until recently, marketing decisions were primarily informed by an analytics approach which relied on large, centralized data warehouses.  Increasingly, marketers are turning to so-called “mini” data warehouses to augment and enhance their understanding of customer behavior.

That means effectively juggling and integrating data from traditional sources, as well as these new ones, including everything from Google Analytics to CRM tools, email service providers, social media sites (especially Facebook and Twitter), SEO platforms and chat applications.  It also means that marketers, in addition to analyzing data, must find those new sources which are most useful to their businesses.  Doing so will help them make more informed decisions, enhance customer experience and push customer interest.

2.  The Rise of Wi-Fi Marketing Analytics

One of the reasons for the success of ecommerce businesses is their ability to collect data about online shoppers.  Brick and mortar stores, lacking this capability, have found it difficult to effectively compete.  That’s where Wi-Fi analytics can help.

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Simply explained, most consumers carry some sort of Wi-Fi device, like a smartphone.  Those devices send out signals as they attempt to connect to networks.  Using Wi-Fi sensors, brick and mortar stores can use those signals to gain valuable data insights about in-store customers, such as where they went in the store and how long they stayed there.  Collected data can then be organized to better understand customer behavior, and to make changes which best accommodate that behavior.  The best of these tools essentially “level the playing field,” helping brick and mortar stores more effectively compete with online stores.

3.  Letting Artificial Intelligence (AI) Do the Heavy Lifting

The speed with which data enters businesses is accelerating, making it increasingly difficult for human beings to effectively process it.  For this reason, more businesses are utilizing analytics programs which include an artificial intelligence (AI) capability.  When effectively leveraged, these new systems rapidly identify customer data patterns and trends to provide guidance on best approaches to performance optimization.

4.  Translating Complex Analytics Concepts into Simple Language

In the past, analysts were expected to crunch numbers and generate reports, but little more.  The problem has been that decision makers, often lacking experience in some of the more obscure concepts which inform analyses, haven’t sufficiently understood what was in those reports. 

Increasingly, analysts will need to provide context and meaning so decision makers can effectively use the insights they provide. That means, among other things, cleaning and polishing data, rendering results in easy-to-understand graphs and charts, and translating results into narratives that key decision makers can use to more effectively do their jobs.

As new technologies and data sources emerge, marketing continues to evolve, as do the tools marketers use to measure the effectiveness of their activities.  Forward-leaning businesses understand the need to keep pace with these changes to succeed in an increasingly competitive business environment.  

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Restaurant Marketing FAQs

What is Restaurant Marketing?

Restaurant marketing is the process of getting people to visit your restaurants. Restaurant marketing creates loyalty, provides data to research, analytics, and allows restaurants to gain a better understanding of their ideal customer profile. It utilizes all customer channels: guest WiFi, website, social, rating sites, mobile apps, email, text, and advertising.

Learn More About Restaurant Marketing Here

What is WiFi Marketing?

WiFi marketing is a marketing technique that uses guest WiFi to collect & clean customer data such as names, emails, phone numbers, customer behavior, and demographics. This data is used to personalize marketing campaigns to increase customer loyalty, build online reviews, and save at-risk customers. The performance of every campaign can be tracked down to the tangible ROI of a customer walking back in your door.

Learn More About WiFi Marketing Here

What is Restaurant Reputation Management?

Restaurant reputation management is the process for restaurants to manage customer feedback and creating systems to improve customer experiences, passively build positive online reviews, and save at-risk customers. It is a very important aspect of running a successful restaurant business.

Learn More About Restaurant Reputation Management Here

How Does Bloom Identify and Bring Back Lost Customers?

Bloom Intelligence uses machine learning to identify at-risk customers. When one is recognized, the system will send them a message with an incentive to get them to return and re-establish their visit pattern. Bloom users are seeing up to 37% of churning customers return.

Learn More About Saving At-Risk Customers Here

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