How to Optimize Your Brand’s Mobile Strategy, Part 2

Filed in Mobile Engagement, Mobile Marketing by on May 11, 2016 0 Comments

This post originally was published as an article in Hot Topics.

Part 1 of this mobile optimization for brands series introduced the concept of the customer journey, its relevance to your company, and why the mobile channel is best-placed to foster engagement between you and your customer.

This initial stage should have provided a clear vision of how you want your customer and your brand to interact with each other, and how you want to map out their journey alongside your brand.

From there, objectives should have been created to match and measure that journey, at which point a working mobile data strategy can allow you to secure the right tools, technologies and teams to build your mobile marketing campaign.

Part 2 of this series follows these tasks up with the digital feature that brings your campaigns to life: data, and your mobile data strategy.

The existing customer data you hold and the subsequent analytics you receive from your campaigns form the bedrock of your strategic decision making, adaptation and ultimate success. Consequently, it’s important that all data sets are ready to be measured and analyzed appropriately.

This means that all data should be of the highest quality: ordered, clean and valid.

Data that doesn’t have these characteristics is generally termed as dirty, or bad. And one of the most common mistakes we find at Syniverse is that a lot of companies initially consider their customer data sets as structured and useful, when in fact they are unusable and dirty.

This can cause problems down the line when you migrate this information into a customer relationship management (CRM) system to help launch your new campaign.

Avoiding dirty data
Data can come in many forms, depending on what your mobile data strategy is, and what your aims and objectives are. The way to avoid bad data within your legacy data sets is to ensure they are properly cleaned.

Cleaning data includes analyzing what you’ve got and spotting mistakes or gaps in any of the entries. Specifically, there are two main types of dirty data: orphaned or missing data; and invalid or unclean data.

Missing data sets are so named because they either have program or system errors associated with them, or simply because they are pieces of information that were considered optional in legacy systems but now need to be mandatory for your new CRM system.

Unclean data sets can have data entry errors, can be duplicated or can have missing data, and they can therefore be invalid to your new mobile data strategy.

For example, one of the common data types we help with are mobile phone numbers. If a number is now inactive, or entered incompletely or incorrectly, it’s unclean and cannot be used to expand your mobile marketing strategy.

Cleaning data is the act of identifying errors, removing or replacing them, and coming up with a system or network that reduces the likelihood of bad data being created again.

In this way, future data quality can be controlled by creating metrics that data management policies can use to demonstrate improvement. There are also tools available today that provide those metrics and can be used for tracking and isolation.

Other considerations should be whether these numbers are mobile, landline or voice-over IP (VoIP), assuming the numbers are active.

Knowing this will allow you to segment and target the right mobile audience after complying with regulatory requirements. This will be covered in more detail in the next part of the series.

Opt-in mobile marketing campaigns
Specifically within the context of mobile strategies, the fundamental part of this process is determining proof that the customer wants to be a part of the campaign.

During the cleaning process, you should ask yourself the following question: What was the last known agreement in which your customer gave consent for participating in a mobile campaign?

Getting the answer to this forms one of the last parts of your data cleaning process.

As you prepare your mobile data strategy, your data sets need to be ordered and correct so that the customer information you hold is usable and appropriate. Verifying the neatness of this data also helps ensure that only those customers who have agreed to be included in the marketing campaign are included.

What’s more, there are some simple steps that you can make to improve the likelihood of your customer opting in to a campaign. These include point-of-sale offers and discounts, in-app push notifications, and social media posts.

Bringing together prospective and current customers, however, needs relevant data to kick-start its marketing potential.

If done right, the results can be powerful.

Around 90 percent of text messages are read within minutes of delivery, for example, making targeted mobile marketing campaigns some of the most effective forms of communication between brand and customer.

Clean data ensures that you are contacting the right numbers in the most appropriate way, and that only those customers that want to be contacted are contacted. Moreover, your mobile data strategy improves the efficiency of your mobile marketing campaign and provides a solid base from which to grow your opt-in customer base.

The next article in this mobile optimization for brands series will focus on the third step of your journey: data applications and methods. Once you have discovered your usable data and had it cleaned and made ready to be used, how do you use it? Get ready to learn more about segmentation.

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About the Author ()

Mary Clark is a former Chief Corporate Relations Officer and Chief of Staff at Syniverse.

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