How Can Data Help Multichannel Marketing?

Quality data and the proper interpretation is the foundation for any campaign. Especially for campaigns using more than one more channel. Customers now want personalized messages and interaction in the channel of their choice. The only way to do that successfully is have ongoing campaigns, spread out of many channels. How can data help?

Step one for any campaign is to know your customer. Dig through all your first party customer data to create personas of your target customers. Knowing about your customers will help you figure out what channel they will mostly like to engage through.

It is helpful to give your customers and potentials multiple touch point options. Encourage your customer to sign up for emails, interact on social media, and any other engagement options. The more data you have on your customers, such as email, phone, social accounts, and their address the easier it will be to engage in a way that reaches them.  

Next, you can collect data and analytics about your customers from the channels you are using to constantly to learn more about your demographics and your customer’s buying habits. The more channels you use the easier it will be to learn about your customer.

Knowing what channels your customers prefer can save you money. For example, there’s no point in sending your target audience direct mail if you know that’s not a channel that works for them. Therefore, collecting data about your customers can help you lower your CPA and give your faster ROIs.

How are you using data to inform your multichannel campaigns?

Want to learn more about how customer data can help your business grow?

 
 

Can You Trust Your Data?

No matter how good the machine you build it is, it’s not going to run well if your fuel is poor quality. Data is the fuel the runs all marketing. The problem is, data error is commonplace. Data providers are all trying to give their clients the best data possible but often the data they are providing doesn’t match up.

Harvard Business Review did a study in May 2020 to test the accuracy of consumer data. They found that the accuracy of Demographic data was particularly disappointing. Most were only around 50% accurate. For example, the average accuracy of gender segments classifying males was only 42.5%.

“Half the money I spend on
advertising is wasted; the trouble is,
I don’t know which half.”

– department-store magnate John Wanamaker (1838-1922)

What do those errors mean for your campaign? A study by Forrester Consulting on behalf of Marketing Evolution in fall 2019 found that “marketers estimate that 21 cents of every media dollar spent by their organization in the last year was wasted due to poor data quality, which translates to a $1.2 million and $16.5 million average annual loss for mid-size and enterprise organizations, respectively.”

How do a few errors on a record level equal so much wasted money? Record level data is the spring from which everything else flows. For a small example, if a few records in your CRM have mistakes in them, what is going to happen when you use them to send an email campaign? The opens and clicks from your email campaign may inform your audience for your social campaign. In every step and trigger along your campaign, the small errors effect more and more information downstream.

The company Truth Set is looking to minimize error across all data. How? By providing a third – party consumer report type service for data providers and marketers. Truth Set goes through every record available and assigns a percentage rating to all the attributes, such as ‘percentage that this record is actually male.’  That way, marketers won’t waste money on inaccurate data and data providers can keep their data clean.

The hope is that as more time goes on, third party quality checks and ratings will become commonplace and shrink margin for error unilaterally.

What can you do make sure your data is accurate? There are a few easy identifiers you can check for. How old is the data? How transparent is the provider with where the data came from? How consistent and relevant is it?

What changes do you think would make it easier for marketers to trust their data?