With today’s digital marketplace, businesses have never had so many options for getting their marketing messages in front of the public. Still, that doesn’t mean digital marketing is “easy.” As new platforms, tools and technology emerge, best practices in marketing change. Consumers are becoming more and more savvy to anything that smacks of a gimmick, and their expectations for meaningful, personalized outreach continue to rise.
However, every audience is not the same, and meeting your target customers where they are and providing the unique information they need to know is essential to success. With all the options out there, it’s important for companies to not only keep up with the latest trends in marketing but also to carefully track which marketing methods, platforms and messages work best for them. And in this digital world, it’s not only the outreach methods that are changing — trends in marketing analytics are evolving as well.
Businesses looking to develop a well-rounded, accurate view of their marketing efforts need to blend old-school detail work with modern tech tools — all while being aware of the legal and ethical responsibilities that come with digital interactions with customers. Below, five industry leaders from Business Journals Leadership Trust share smart strategies to help you navigate modern marketing analytics.
1. Create target audience profiles.
Get to know your audience on a granular level, and do it by hand. Create samples of your target audience profiles, and then look through those profiles to see what they value, care about and love (and love to share). Doing it by hand as opposed to doing it via artificial intelligence is key. – Christopher Tompkins, The Go! Agency
2. Assign different weights to different platforms.
True cross-channel media optimization continues to be more and more critical. Media platforms are shifting on a daily basis, and your marketing efforts must constantly add different weights to different platforms to meet your brand’s KPIs and acquisition goals. – Jessica Hawthorne-Castro, Hawthorne Advertising
3. Expand your use of AI.
The next big trend is definitely expanding the use of artificial intelligence. As AI becomes more sophisticated, marketers will apply it more and more for things like a detailed analysis of customer behavior, predicting customer needs based on behavioral patterns, and targeted marketing messaging and campaigns. To prepare, businesses need to make sure they have agile marketing teams ready to adapt to any change. – Peter Abualzolof, Mashvisor
4. Integrate predictive analytics.
While digital marketing is maturing in some aspects, it is still an evolving discipline. I believe the next wave is integrating big data, predictive analytics and machine learning to target “what’s next” in a customer’s purchasing lifecycle. Doing so will allow organizations to predict a customer’s next purchases and influence and market to them even before they realize they have a specific need. – Quoc Nguyen, Arthur Lawrence, LLC
5. Stay up to date with changing laws.
The changing landscape of digital and data privacy laws will continue to challenge marketers. Understanding how to collect data in a compliant way in all the jurisdictions in which you do business, while still achieving relevance and personalization, will become even more of a balancing act. – Jen McClure, 2GO Advisory Group
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“Your company is your employee. We’re moving towards data, analytics and automation but still it’s the people behind all those programs. The employees and culture are the most important thing to any company.“
Author: Jessica Hawthorne-Castro, CEO Original Link:Digitizing Polaris Date Published: July 21, 2017
One of the most pressing challenges facing modern businesses today is shifting culture towards a data scientist-driven approach. To complete this shift, a company doesn’t just need a big data strategy, it needs a business strategy that incorporates big data in a way that empowers all employees to think like data scientists.
Developing the Data-Driven Business
For businesses, “big data” means taking information from various disconnected sources and using analytics tools to combine it and find new insights. It’s about correlation, discovering connections between events or information that were previously hidden
For the modern company, data is the most important asset, one that is dynamically changing. Valuable data sets come into play every day from mobile-based customer interactions to the billions of internet-connected “things” coming online. The trick for companies is to develop smart strategies to leverage this flood of data and to monetize data while improving customer loyalty and overall experience.
Three Tactics to Become a Data Driven Company
To think differently, you must create a business strategy that focuses on data, one driven by the leaders of the organization. Adopting a big data approach requires a cultural shift within all levels of the organization. For example, training is crucial for the adoption of big data-focused analytics tools. Staff members must understand how to look at large data sets and pull insights, whether they are based on customer behaviors, problems in the supply chain, or adjustments that could be made to operational processes.
The training needs to include actual sessions with the technology to showcase the ease of use and how it relates to the specific individual’s role within the company. It also needs to be backed by management and include a shift to a more of a real-time and cross-departmental structure. This requires management to loosen some of the constraints of the traditional business hierarchy, where previously insights had to go up a lengthy chain of approval or review. There are still checks and balances, but there is a shorter cultural pathway from finding out something is wrong to implementing a data-driven fix.
Another cultural change involves ceasing to make decisions and taking action based solely on “gut feelings.” This doesn’t limit intuition or innovation, but it mitigates risks and uses hard data as a way to augment creativity and decision making. It’s a model with built-in accountability as staff members need to justify their recommended actions with data.
Make Real World Changes through Data
Companies need to make dramatic data-centric shifts because of the real, impactful changes it can produce. Big data will drive change and induce innovation in your company as well as spur growth, improve agility and foster communication. And data is the most important asset to monetize drive loyalty and safeguard privacy and security.
Consider these examples of the direct impacts of a big data approach:
Review financial data to uncover incidences of fraud or potential risk
Refine a product’s attributes based on customer-derived data and sales numbers. For example, analyze how customers run on an IoT-enabled treadmill and adjust its construction accordingly.
Analyze target markets to see if demographic parameters are off-base and make adjustments
Enhance the customer experience by offering deals and retention strategies that are backed by data insights
Develop surprising new marketing initiatives and targets through review of sales data combined with customer service data, and other sources
For an industry-specific example, the advertising industry is rapidly changing due to new streams of data. Advertisers and broadcasters crave detailed metrics on viewer behaviors, they are now receiving new details via “second screen” behaviors. A significant number of consumers utilize their phones or tablets while watching television and their actions during this time provide advertisers with rich data about likes and dislikes, which can then be used for more targeted advertising campaigns.
Build a Big Data Strategy
In order to reach the goal of a data-driven company, every firm needs a big data strategy that breaks from conventional thinking. This is a whole business initiative, not an IT initiative; however, IT should enable your result. IT should be at the lead of the implementation as they control the information, but multi-departmental cooperation is a must. Remember the business goals always comes first. Develop a sound business strategy that incorporates data as a core element, with a focus on how staff members make decisions that ultimately impact the business. Augment these decisions with rich and multi-sourced data, and you’ll create a company that can move with agility beyond its competitors.
Here are some of the key steps for creating a data-driven company:
Assess pressing business needs. What are the unknowns about products, customers, and internal processes?
Map out your data architecture and identify all data sources. Can you pull together customer service chat transcripts along with website visitation metrics? You need disconnected data sets.
Determine how to consolidate and relate all of the data sets.
Develop user-friendly solutions to analyze the data to help you find surprising insights. Data should drive discovery. Let users play with the information in new ways. Rigid Excel reports and big data analytics do not mix.
Turn those insights into actions.
Consistently look to bring in new data sets.
There are some of the caveats of the big data approach that companies should be cognizant of so that the can make informed strategic moves. An over- reliance on big data shouldn’t remove personal communication between the company and consumer. And, while there are many correlations that can be uncovered through big data analysis, there’s still a human element needed to confirm that the correlations make contextual sense to the business. It’s still an “art and science” approach, ideally one where the data makes decisions better and faster.
And don’t worry if you are just entering the big data game. The truth is that cost effective data management wasn’t available until recently, so it is not unusual to have a ton of data in many, many different places. As you begin, make a goal of centralizing your data so that scalability to meet the needs of your business is less complicated down the road. The amount of data gathered increases exponentially; so, you’ll be glad you started now.
Jessica Hawthorne-Castro is the CEO of Hawthorne Direct, a technology-based agency specializing in analytics and accountable brand advertising. She has strategically positioned the agency to be at the forefront of the marketing paradigm where art meets science. Throughout her tenure with Hawthorne Direct, she has fostered long-standing relationships with the company’s expansive base of diversified clients resulting from an unwavering commitment to unparalleled service. Her role principally involves fostering long-standing client relationships to develop highly strategic and measurable advertising campaigns, designed to ignite immediate consumer response.
Editor’s Note: The following is a guest post from Jessica Hawthorne-Castro, CEO of agency Hawthorne.
As marketing budgets have soared, so has the need for accountability. For direct response marketers, this comes as no surprise.
The 2016-2017 “CMO Spend Survey” from Gartner found that marketing budgets increased to 12% of company revenue in 2016 and CMO marketing technology spend is on track to exceed CIO tech spend in 2017. Along with all this money flowing in comes the need for marketers to demonstrate results — to prove that those marketing dollars are dollars well-spent. This has made accountability a buzzword and guiding principle for forward-thinking marketing teams, but it’s a principle that has guided direct response (DR) marketers for years.
DR is an accountable, measurable and actionable way for brands to engage and collaborate with their customers. Digital marketers may have made accountability cool, but DR pioneered the concept when most brands were pouring all of their dollars into image-based advertising.
It’s a mixed-media world
The proliferation of mobile devices has made marketing more complicated and results more difficult to track. Between traditional media like linear TV and newer formats like social media and mobile apps, brands have to market across channels and embrace mixed media campaigns to stay relevant. However, campaigns across multiple channels are more difficult to measure than campaigns across just one. Organizations that value accountability need tools that can measure and track across platforms.
One of the tools driving the shift towards accountability is marketing automation, which is used to market across multiple online channels, automate repetitive tasks and measure and track results. It gives organizations the tools they need to measure mixed-media modeling, which involves analyzing sales data to determine the effectiveness of the marketing mix. Today, half of companies use a marketing automation solution, which is more than 11x the rate of use in 2011.
We’ve observed this surge in our own work at Hawthorne. Marketers are using technology applications and platforms to figure out how much “weight” to put on which advertising channels, which platforms are performing the best (and worst) and how to effectively allocate budgets among those various options. These insights can then help determine the optimal media mix, which can be a major challenge in today’s highly-fragmented digital media world, where consumers hop back and forth between devices and view ad content on each.
One solution is pixel technology, which can be used to figure out and track consumer engagement levels. This technique means adding pixels (or small lines of code) to client websites to track the consumer from beginning-to-end on a website in order to timestamp when they first saw the advertisement and then went directly to the website (or Googled it). Then it tracks their path through the website on each individual page and even creates a heatmap of what they are looking at on each page. This tracks consumer engagement and whether or not they made a purchase.
Accountability and automation
The combination of DR and marketing automation enables advertisers to seriously up their accountability. They can use an array of online and offline media choices to quickly capture engagement and directly pitch clients at the top of the sales funnel who are happy because they know their target audience is on the radar.
For example, Hawthorne recently created a campaign that targeted 18- to 25-year-olds, who represent around 10% of people in U.S. households with TVs. Hawthorne created a customized marketing plan that incorporated programmatic and addressable TV and targeted that specific demographic, similar to how online media can target specific niches. We then used mixed-media modeling to demonstrate the campaign’s impact across multiple platforms and to maximize the advertiser’s message across channels.
It was a highly targeted and highly accountable campaign that resonated with the audience and the client alike.
The learning curve
Automating and fine-tuning a campaign is a big challenge that involves a learning curve with new technology and requires research upfront, particularly in terms of identifying and leveraging cross-device segmentation.
Marketers have to figure out the paths that different demographics are going to take, as well as understand how quickly they will move through the sales funnel and make a purchase or sign up for a service. In addition, marketers have to identify which advertising mechanisms work the most effectively in each case. Or, to put it more succinctly, marketers have to understand how people respond from one end of the sales funnel to the other, including how they engage across devices. It all boils down to understanding the lifetime value of each customer and connecting that information back into advertising, station selection, creative and any other element that has an impact on sales.
The more consumers use multiple devices to consume content, the more marketers will embrace mixed-media modeling (and related tools) to automate certain parts of the advertising process. And, as consumers embrace more ways to connect with their favorite brands, agencies and marketers will have to evolve along with them, or fall behind. Greater accountability in marketing is better for everyone.
Organizations are increasingly adopting big data analytics to understand and then fix business problems. They’re learning how to extract value from multi-sourced information and then relate that information to an issue in their marketing, manufacturing, advertising, or shipping, etc.
For example, T-Mobile (and the other main carriers) consistently use big data analytics to spot the reasons for (and prevent) customer turnover. Customer attrition is a significant expense in this industry, so firms that can best use data to improve retention and improve satisfaction will have a leg up on the competition. Big data is not just a tool for the enterprise level firms. It’s appropriate for businesses of varying sizes that want to better understand customer behaviors and improve their marketing tactics.
Every organization wants to use data to find actionable insights. It’s “cause and effect” on a broader scale, where there could be multiple factors working in concert that are producing a certain result. The difficulty is in generating the right data, keeping it organized, and then having the right analytics tools and staff members who know how to extract correlations. Doing this right to optimize the customer experience and boost sales requires adherence to several best practices:
Use Statistical Modeling
Marketers working on TV campaigns now have at their disposal a number of modeling tools to help them gauge performance. They can use customer demographics, Nielsen-derived viewing data, airing size, and specific data on the actual stations utilized and the airing timeframes. Marketers can use this clean data to gauge current performance and then dynamically adjust future campaigns accordingly. There can be surprises uncovered in this process, as marketers might find for example a previously under-served demographic that is generating impressive sales in response to TV campaigns.
Clean up the Mess
The “mess” in this context refers to data that is not properly structured and is essentially useless when it comes to analysis. Even the best data scientist and marketing wizard can’t pull insights from broken data. Do some work on the front end to ensure all of the data streams coming into the analytics tool are organized and clean. Attempting to fix data after the campaign is launched is a time-intensive process that doesn’t give the marketers a chance to suggest campaign changes in real time.
Follow Customer Behavior and Actions
Companies have at their disposal a powerful but often underused source of rich data. It’s the touch point for most customers – the website. Whether it’s a landing page, mobile site, or the primary corporate website, all of these channels offer a wealth of information. Firms can track this information through pixels that can be placed throughout the sites to measure customer behaviors, from what they visit to how long they hover the cursor over the “add to cart” button.
Understanding customer behaviors provides unbelievable context and the opportunity for segmentation. Marketers should develop “playbooks” on consumers so they can then be grouped together in new and surprising ways. Tracking should also include device information, especially as consumers move to a mobile-centric way of communicating and ordering. Social media tracking provides another layer of data on how customer’s share information about a brand and gives marketers a way to reach social media influencers.
Measure the Retail Responses
Big data analytics is essentially a new way to look at “cause and effect.” Instead of reviewing a direct mail piece’s performance against actual sales, big data analytics can correlate seemingly unrelated company actions and customer actions.
Marketers should focus on the retail responses amongst the various channels to spot these surprising correlations. With many campaigns, there can be a brand boost in sales for a campaign that is meant to only promote a single product. For example, TV spots about the durability of a housewares maker’s blender could drive sales of the firm’s carbonated beverage machine. Marketers will need to use analytics to look deeper at the behaviors and actions behind such results, and then adjust accordingly to boost sales of both products.
The ROI of campaigns can now have multiple layers, so marketers should understand how to analyze campaign impacts on a deeper level. Marketers that take a measured (yet creative) approach to big data analytics will be the ones most likely to uncover surprises and be able to prove campaign ROI. Doing this right requires some patience and hard work on the front end to introduce clean data, build structured analysis models, and create analytics that are built for multi-channel environments.