Anchoring Marketing Analytics To Strategy
The company’s chosen analytical options should be based on its overall strategy. A strategy anchor is a way to keep your company from allocating marketing dollars based on the previous year’s budget or on what business line or product fared well in recent quarters.
The approaches can become competitions based on who has the best proposal or who is the most vocal, rather than what needs to be grown or defended.
Policymakers should focus on the return, value, and payback of potential projects to make more informed decisions.
When you are considering different options, it can be helpful to use a score to help you compare them. This score can take into account different factors, such as how much money you are already spending, whether there is a minimum amount you need to spend on certain things, and whether you have already made any commitments.
The key to creating a successful MROI portfolio is understanding the purchasing habits of your target consumers.
The way consumers behave has changed so much in the last 5 years that the traditional ways of thinking about them, such as the marketing “funnel”, don’t really make sense anymore.
Rather than prioritizing brand awareness, the consumer decision journey recognizes that the buying process is more dynamic and that consumer behavior is subject to many different moments of influence.
The sidebar “Five questions for maximizing MROI” provides additional information on how to get the most out of your MROI.
Making Better Decisions
1. Identify The Best Analytical Approaches
To create an effective marketing mix, organizations need to compare the advantages and disadvantages of the various tools and methods available to them, to see which ones best fit their strategy. When it comes to non-direct marketing, the prevailing choices include the following:
Advanced analytics approaches such as marketing-mix modeling (MMM)
MMM uses big data to determine where spending is most effective.
This approach statisticall connects marketing investments to other drivers of sales, and often includes external variables such as seasonality and competitor and promotional activities, to uncover both longitudinal effects (changes in individuals and segments over time) and interaction effects (differences among offline, online, and — in the most advanced models — social media activities).
MMM can help with both long-term strategic planning and shorter-term tactical planning, but it isn’t perfect: it requires detailed sales and marketing data going back several years; it can’t measure things that don’t change much over time (like out-of-house or outdoor media); and it can’t measure the long-term effects of investing in a specific touchpoint, like a new mobile app or social-media feed.
MMM requires users who understand econometric models and can use a scenario-planning tool to model budget implications of spending decisions.
Heuristics Such as Reach, Cost, Quality (RCQ)
This company breaks down each customer interaction into its separate elements, like how many potential customers were reached and how much it cost per person, using a combination of data and intuition.
It is often used when MMM is not possible, such as when there is not a lot of data; when the rate of spending is similar throughout the year, as is the case with sponsorships; and with media that is always available where it is harder to isolate the investment effects.
The RCQ allows touchpoints to be compared more easily by bringing them back to the same unit of measurement. This task can be completed easily, usually with just a Excel model.
It can be difficult to know how much each touchpoint is worth when there are different types of channels. RCQ is not able to take into account network or interaction effects and is very reliant on the assumptions that provide it with information.
Emerging Approaches Such As Attribution Modeling
As more and more advertising dollars are spent online, it becomes more and more important to be able to attribute results to specific online media buying and marketing campaigns.
Giving credit to online touchpoints that lead to sales is what attribution modeling is all about. This includes things like emails, ads, social media posts, and websites.
Credits can help marketers see which online investment activities are driving sales the most.
The most common scoring methods give all the credit to the last touchpoint before conversion.
Newer methods that use statistical modeling, regression techniques, and sophisticated algorithms that tie into real-time bidding systems are gaining traction because they are more analytical.
Methods that depend on cookie data limit the accuracy of data sets, making it difficult to attribute the importance of each online touchpoint.
2. Integrate Capabilities To Generate Insights
The best results come from using multiple MROI tools together, not just relying on one.
An approach that includes gathering data and insights from direct-response sources reduces the biases inherent in any one MROI method and provides business leaders with the ability to shift the budget toward activities that produce the most positive results.
So how do these techniques work together? A company may discover that a large portion, often 80 percent, of its marketing budget is spent on TV, digital, print, and radio advertisements.
It’s beneficial to use MMM for activities that generate data that can be tracked over a long period of time.
If digital spending is broken down into smaller categories, it can be easier to see which activities are most likely to lead to conversions.
The company could then use heuristics analysis such as RCQ to monitor the remaining 20 percent of its spending. This remaining spending may go toward sponsorships and out-of-home advertising to capture the company’s non-TV-watching target audience.
It is helpful for marketers to be able to compare the results of different analytical techniques by having common response curves.
The organization can then use a decision-support tool to integrate the results, allowing business leaders to track and share marketing performance on a near-real-time basis, which would allow for less guessing and more knowledge-based decisions.
A power company used RCQ analysis to figure out which type of advertising (out-of-home or sponsorship) would be most effective in reaching their target audience. This analysis increased the efficiency of marketing communications by 10-15%.
After realizing that its spending on digital media was not as effective as it hoped, the company decided to get a more specific assessment of its Return on Investment from MMM.
The study found that for every €1 million invested in online advertising, 1,300 new consumers were generated, whereas the same investment in TV, print, and radio helped the company retain 4,300 consumers, 40 percent of whom were likely to stay loyal to the brand over the long term.
The company was able to understand where to spend its money and resources to both bring in new customers and keep the ones it had.
It can be tempting to spend money on short-term initiatives that have high ROI when you are trying to improve the mix.
The way that brands collect data creates bias because most of the data comes from consumers doing things like signing up for brand-related news and promotions on a smartphone or buying a product that is on sale.
The short-term effect typically makes up 10 to 20 percent of the total sales, while the brand, which is a longer-term asset, makes up the rest. Businesses need to ensure that their sales models can predict short-term and long-term sales outcomes.
A food brand was almost tricked by a short-term trap. The company ran a campaign that included Facebook ads, contests, photo-sharing incentives, and shared-shopping-list apps.
This approach to marketing is much cheaper than traditional methods, yet it still generates comparable sales results. Traditional marketing methods include expensive television commercials and print ads.
After careful consideration, the brand decided to shift spending from TV and print advertising to social-media channels. When long-term effects were considered, the company’s digital efforts were only half as effective as initially thought.
If the company had significantly reduced its TV spending, as MMM had suggested, it would have reduced the brand’s profit.
Marketing analytics is no longer one-sided, with companies using data to improve revenues. Instead, companies are now focusing on messaging and branding for a large consumer segment, typically a geographical or demographic segment.
The analytics were looked at to see how the product had performed over a period of several years, taking into account various factors. The digital age, with its superior technology, has shifted the focus of marketing analytics to personalized insights and measurements that are nearly real-time.
Significant shifts in marketing analytics were caused by:
Social Advertising Explosion
across devices, marketers have to adopt a complex and complex array of tools Each platform has its own set of ad products and targeting capabilities, so marketers have to adopt a complex array of tools to connect and engage with customers across devices.
Ubiquity of Mobiles
As of 2022, nearly 83% of people on earth own mobile phones. Behavioral and emotional data is captured best when brands allow for engagement through the modes that consumers prefer.
Video-Centric Marketing
With a mobile phone, anyone can create and stream videos to promote products, themselves, influence customers, deliver tutorials, and review anything. They are leveraged to improve conversion.
Digital-First Generation
Since millennials prefer to use digital media for engagement, they expect instant interaction, gratification, and customer service.
In order to grow their product, organizations need to engage their audience with content, interactive ads, and recommendations. Analytics generated from these activities will help organizations to meet customer expectations.
Data is Big
Omnichannel decision influence is a marketing strategy that uses data gathered from every step of a customer’s journey to generate real-time insights. This enables businesses to deliver targeted ads, offers, and enhanced modes of customer service through artificial intelligence and superior customer service.
Marketing analytics can be used to drive product growth by using methods that traditionally measure things like customer behavior and preferences. These are:
Mixed Media Modelling (MMM)
This method allows marketing teams to see how well their marketing campaigns are doing and how much influence they have. MMM can help you understand trends over a long period, such as seasonality, brand equity, and holiday effects.
MMM does not work well with granular data, does not take into account relationships between channels, provides limited insights into brand messaging, and does not take customer experience into account.
MMM is still relevant today and is often used alongside customer-centric analytics models to help organizations identify general broad-based patterns and trends over the course of years.
Multi-Touch Attribution (MTA)
A method of marketing that takes into account a customer’s journey and assigns a value to each stage in order to determine which aspects of the journey are most important in driving a conversion.
Companies use marketing campaigns that target both online and offline channels to generate leads and sales. These channels can include social media, business websites, print media, billboards, streaming platforms, and physical events.
It’s difficult to measure progress across these areas, but using MTA across the customer journey provides detailed data that can be analyzed. The customer journey is complicated because customers use different devices, platforms and channels before making a decision.
Tracking their engagement is tricky, given data privacy concerns. Those in marketing need to keep an eye on key performance indicators and models, putting together teams and using analytics software to help make sense of it all. This will require regularly applying any insights gained and making sure marketing strategies are always optimized.
MTA is used on digital campaigns that use A/B testing and automation platforms.
MTA models are being challenged because analysts lack offline data to apply to the models, have limited visibility into external factors like seasonality, and have difficulty effectively wrangling data given the complexity of the data and models.
Unified Marketing Measurement (UMM)
This method uses a mix of different attribution models to arrive at comprehensive marketing metrics. This data helps us decide how much money to spend on ads, which then should result in more people completing the desired action. Impactful UMM needs a unified analytics platform that can:
- Conduct data audits
- Continuously evaluate metrics for in-campaign optimizations
- Choose right data and models to feed into the platform
- Continuously discover the best models through a cycle of test and learn
The benefits of UMM include:
- Online and offline data visibility – combining MMM & MTA data
- Integrated data view
- Real time insights and optimization to quickly pivot campaigns
- Personal and aggregate level data
Marketeers need to consider holistic marketing performance driven by key metrics such as brand perception data, in-campaign insights, granular consumer-specific data, and long-term aggregate data along with strategic and carefully planned implementation to drive growth using the right unified marketing platform, tools and teams.
Conclusion
If your organization is trying to digitally transform, it’s important to be agile so that you can respond quickly to changes in technology and business. Organizations must be innovative in order to exceed expectations in the digital age.
Businesses will need to be able to sense, learn, respond, and evolve like a living organism in order to be successful in the future. A suite of services that is comprehensive yet modular is doing exactly that.
Live Enterprise is a software company that provides organizations with tools to make better decisions, work more efficiently, and stay up-to-date on data. Their goal is to create connected organizations that can innovate together for the future.
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