RMM Interview – In Conversation with Dennis Williams, Director of Content and Brand at Marketing Evolution

Interviews

At Right Mix Marketing, we plan to chat with the thought-leaders in the digital marketing, business and entrepreneurship space. This is the first interview where we’re discussing with Dennis Williams, Director of Content and Brand at Marketing Evolution on marketing analytics, brand tracking, and deriving insights from big data.

Q1 – What are three ways marketers can use data to strengthen their campaign efforts?

The goals of leveraging data to inform marketing campaigns are to ensure that your customers have a good experience with your brand, and that the campaign produces results – whether defined as a form submission, a sale, or otherwise. There are three ways you can use data to achieve these goals.

1. Audience Segmentation:

The key to creating positive experiences for your customers, which drive them to engage with your brand, is understanding their interests. Marketers can use data to understand consumer needs and interests. For example, a production company uses second-party data to learn that a segment of their target audience has gone to an average of three action movies in the last six months. Additional data might show that a portion of these consumers live in the Chicago area. With this insight, marketing teams can create better localized content,  prompting customers to see the latest action movie at a nearby theater. This is content these customers will likely want to engage with, as it speaks to a direct interest with a CTA informed by the user’s location, resulting in a ticket sold.

Marketing teams should use data collected from first, second, and third-party data sources to learn as much about their target audiences as possible and create segments based on interests, device preferences, what type of creative they respond to, and more. Having this information will enable marketing teams to create campaigns that delight users, ultimately leading to the desired outcome.

2. Customer Journey Mapping:

More than just getting to know your customers’ preferences and interests, data can be used for customer journey mapping to determine which phase of the funnel your prospects are in. This will largely come from your attribution data.

When developing content for your target audience, it will be bucketed into top of the funnel, middle of the funnel, and bottom of the funnel categories. Marketer’s data infrastructure should help them better understand when a customer has engaged with one of these pieces of content. Granular attribution data, collected with models like unified marketing measurement, can inform marketers of these engagements at the person level by centralizing and correlating data across all online and offline sources and attribution models. If they can see that one user has engaged through various interaction points at the top of the funnel, they know they should begin moving that user down the funnel.

This improves user experience, as customers’ growing level of interest is being acknowledged in the offers they receive, and increases marketers’ chances of achieving conversion. Looking back to the previous example, if the customer continued to get ads to attend the new action movie after they had already seen it, it would likely annoy them and cause them to disengage with the brand. However, using attribution data to see that this user had clicked the CTA and purchased a ticket, the studio could instead target them with content around behind the scenes footage, or interviews with the stars, to keep them engaged in the brand and franchise.

3. Media Planning:

Finally, data should be heavily relied upon in media planning efforts. Media planning is when marketing teams decide where to spend money in order to reach their audience. For example, should they spend the total budget on a national TV spot, or will they more effectively reach their audience across various digital channels? The answer to this question is in their customer data. Marketers need to look at which of their campaigns have been the most successful – considering channel, creative mix, offer, and audience. Based on this data, marketing teams can pick and choose which elements will make upcoming campaigns the most effective, and allocate spend accordingly.

Q2 – What are the top challenges marketers face when using data?

Three of the most common ways I see marketers struggling with when it comes to data are:

A. Issues with Data Quality:

With different teams collecting information across various platforms, it is likely they will run into issues with data quality – be it incomplete forms, duplicate information, formatting differences that makes the data impossible to sort, or a host of other issues. This is why marketing teams need to focus on establishing collection policies and enforcing data quality dimensions, rather than risk making decisions off of inaccurate or skewed data.

B. Data Correlation / Analysis:

Another common challenge is sorting, correlating, and analyzing such massive quantities of data. Today, organizations can track every click, view, and purchase from customers – and they do. With such big datasets, marketing teams and data scientists become easily overwhelmed with processing and correlating data from different sources, just to prepare it for analysis.

What ends up happening is that by the time the data is prepped, analysts have almost no time left to derive insights from the information before it becomes outdated and irrelevant. This is why marketing analytics platforms have become so essential. Platforms with immense processing power can prepare data from various sources, giving data science and marketing teams more time for actual analysis from which to make decisions.

C. Choosing an Attribution Model:

Finally, the efficacy of marketing data largely comes down to which attribution model you use. It’s not uncommon for organizations analyze their data using attribution models that don’t offer a complete picture of the customer or the customer journey. For example, media mix modeling will give a good overview of historical campaign performance and external factors, but does not offer insight into individual consumer preferences or engagements.

To ensure you get the most from your data, be sure to consider they types of insights you need in order to optimize your campaign, and select an attribution model that can offer metrics aligned with those KPIs.

Q3 – What are the most important thing brands need to consider when allocating their media funds?

The most important thing to consider when allocating funds is: Will my message reach the target audience on this channel? If your campaigns are getting a lot of impressions or engagements among a consumer base that isn’t interested in your product or service, it will not generate return on your spend. If the answer to that question is yes, the next thing to focus on is messaging – and whether you have created an ad or content your audience will be interested in engaging with.

Q4 – How do brands choose which channels are driving the most results?

Brands don’t. Consumers do. Marketing teams distribute messaging across a wide media mix and if you have done a good job building out personas and researching the channels those personas use, then you will see results. Not all of the channels you use will have the same amount of success. To get to the bottom of which ones are actually driving engagement and conversions, you need to use advanced attribution models to follow your customers’ path to purchase. If that path consistently begins on one channel, you can reallocate funds to have a greater presence there.

Q5 – How do brands change campaign direction while in-flight and when is it appropriate to do that?

The right time to update a campaign is once you notice it is underperforming. If you wait until the campaign is finished and it did not get optimal results, nothing can be done and budget has been wasted. The challenge with in-flight campaign optimization is getting data and analyzing it quickly enough to make changes while the campaign is still running. To do this, marketing teams need to be working with analytics platforms that can distill sets of big data into actionable insights as that data is collected. From there, they can determine what elements of the campaign are working and what needs to be updated as soon as possible to generate more ROI for the campaign.

Q6 – How can brands create a consistent message/ experience across channels?

First, you want your brand to always be identifiable, so be sure that your messaging and imagery is stylized consistently. When it comes to creating a positive brand experience, data will continue to play a major role. Use data to see what types of brand messaging your users are engaging with, and emphasize similar branded content across channels.

Q7 – How do brands identify which message is resonating with their audience?

First, brands should already have established an identity and brand values that align with those of their core audience. Think of Patagonia – this brand for outdoor adventurers prides itself on being sustainable, transparent, and durable, which corresponds to the values of many of its users. Once this core identity is established, you can develop messaging and creative that speaks to different audience segments. Display these messages across popular channels, collecting data to see which performs best. Based on this data you can optimize your messaging using A/B testing – changing small elements of the creative mix until you have determined the messaging preferences for each audience segment. Looking at these metrics can also help brands determine if there is a certain demographic being underserved in messaging.

Q8 – Is personalization reserved for those marketing products online?

It is definitely easier to personalize an online campaign, but working with multiple data sources allows you to personalize offline campaigns as well. Something as simple as location intelligence allows you to mail personalized offers to customers to redeem at their local store. Using second and third-party data sources such as subscription and mailing lists can give your organization an edge when it comes to personalizing offline campaigns based on geography or interests.

Q9 – What results outside of direct sales should marketers be looking for?

Sales and conversions are often the goal of performance ads. These ads promote short-term deals etc, with a clear call to action to get the customer to convert. However, brand building campaigns can be just as valuable. It is important that you measure the value of your brand when looking at marketing ROI, beyond just direct sales. Brand building campaigns are what allow you to connect with and retain customers for long-term relationships, and can ultimately have a greater effect on marketing ROI than direct sales.

Q10 – How do marketers turn their data into insights?

Starting all the way at the beginning, the first step to turning data into insights is to ensure you have data quality control policies in place. This will give you complete and consistent datasets, which will simplify the process of sorting and correlating large sets of data. From there, I recommend using an analytics platform to centralize, correlate, and sort all of this information – this will ensure your data science team is not overwhelmed, and will give your team the processing speed necessary to make in-flight optimizations. Once you have high-quality data and the ability to distill it into digestible information quickly, it comes down to having the expertise to discern trends that show what is successful, what is missing the mark, or if audience segments have been left out.

A short bio of Dennis Williams:

Dennis Williams II is a two-time LinkedIn Top Voice in 2016 and 2017, and has a proven track record in using content experiences to drive strategic growth. He has over 5 years of experience leading digital marketing teams for both venture-backed startups and media brands, creating digital campaigns and innovative experiences to grow loyal audiences. He is currently the Director of Content and Brand at Marketing Evolution.