By Olivia Cahoon
Data is increasingly important for businesses, but it’s useless if it can’t be leveraged to make better, more informed business decisions. Common mistakes that hinder the ability to properly utilize analytics include employing inadequately trained business users and failing to equip analysts with external data sources. By incorporating customer segmentation, individual customer behavior, and employing the best analytic capabilities, businesses leverage analytics and better understand their market data.
Analytics for Better Data
Businesses leverage analytics to support their marketing automation efforts. With the availability of richer and deeper data on customers and prospects, analytics enable best-in-class-marketing, says John Hagerty, VP, product management, Oracle Analytics. Better marketing includes several factors like customer segmentation, individual customer behavior, marketing campaign performance, and employing the best analytics capabilities.
Customer segmentation identifies target prospects and customer and classifies them into groups that exhibit similar attributes or behaviors. Classifications include income level, propensity to buy, age, gender, and family situation, which can be used to create segments to better market products and services. “Using an individual customer history and attributes to interact with that customer based on their own preferences, experiences, and attitudes is key as well,” says Hagerty.
Online and offline marketing programs are monitored to understand how well they perform. Whether it’s click through from an online banner, advertisement, email solicitation or response to a direct mail piece or targeted email, Hagerty believes knowing the most effective campaign guides futures efforts in this area. “In my experience, leveraging analytics for better marketing is tightly tied to tools. It’s about proactive analytics that drive the business that have a lot of the limelight, and less about monitoring past performance,” he explains.
Sheila DeJoode, customer experience product management, Infor, believes marketers recognize the value of defining a target segment based on geography, transaction history, or customer demographics. “Analytics allow them to segment campaigns even more effectively and with greater nuance and provide the context to serve up the right offer or information, rather than following up based on a best guess or previous experience,” she explains.
Analytics is making a difference is in ROI analysis, especially in large enterprises with geographically dispersed marketing teams where it can be challenging to track. According to DeJoode, different regions and channels often use marketing automation systems that don’t always track and measure ROI in the same way. She believes a true enterprise analytics system helps enforce consistency across regions, lines of business, and channels for a more accurate ROI analysis while still offering different entities and custom capabilities for each user, region, or campaign.
In addition to tracking ROI at an enterprise level, marketers also demand the ability to drill down for deeper analysis at the segment, geographic, transaction, and attribute levels, says DeJoode. “Marketers want to know which areas saw better-than-expected results and which offers fell flat. They want to drive into which offer codes within the same campaign generated higher click-through rates.” This data is analyzed to optimize channel, design, and message strategies to generate better results for future campaigns.
Daniel Brault, marketing solutions senior manager, Qlik, identifies three major areas where successful organizations leverage analytics for greater marketing efficiencies—outbound campaign performance, increased customer centricity, and integrated web and digital metrics for better targeting multi-channel engagements.
Organizations now take a cross-functional and integrated multi-channel approach to respond better to today’s consumers. “These organizations know a successful analytics strategy is more than measuring revenue, leads, and conversions,” says Brault. Today, it encompasses understanding the campaign’s entire story while comparing markets, products, and historical data to achieve the greatest return on investment. “Understanding how all activities impact the entire funnel is invaluable for marketing organizations to better connect with new and existing customers,” she continues.
Industries Leverage Analytics
Several industries successfully leverage analytics to make better informed business decisions. Businesses that are most successful in leveraging analytics for marketing agree upon business-critical processes they want to address, have clear goals, and bring in the appropriate data sources, both internal and external, that help them address those goals, points out Roman Stanek, CEO/founder, GoodData.
“Instead of relying on employees to go to static dashboards to try and interpret the graphs presented, successful companies embed the insights in the applications in which their employees work so there is context and it’s obvious what actions need to be taken or automated,” says Stanek. This includes the independent software vendor (ISV) market. He continues, “ISVs recognize the power analytics have in providing value to their customers as well as an opportunity to increase customer stickiness and potential new sources of revenue.”
The advertising technology (AdTech) industry leverages analytics for prospects including location, browsing history, general preferences, and segmentation data. “They get just a few milliseconds to make a bid on an ad inventory while that website is being rendered by a publisher,” says Matt Bushell, director of product marketing, Aerospike. AdTech companies pull prospect data, analyze, and cross-reference it within their database of customer bid criteria before executing.
Financial services conduct detailed risk modeling and analysis on a regular basis for an integrated, real-time view of market, credit, and liquidity risk exposure across asset classes and customers, says Bushell. To enable analysis and simulations, financial services’ risk management applications integrate, aggregate, and model diverse data sets from multiple systems. “The output from these models help them not only comply with capital and liquidity mandates but also optimize returns using these risk calculations, like risk-adjusted returns.”
Payment service firms use analytics for security and fraud prevention. According to Bushell, payment service firms have pattern-based and machine learned algorithms that detect anomalies within milliseconds to determine in-process whether to deem a transaction fraudulent or not. “The faster they can process metadata on the requestor—location, device, cached data, card usage patterns—the more complex the algorithm they can use and gain greater accuracy,” he explains. Payment service firms require sizeable analytic capabilities to detect irregular patterns via machine learning and artificial intelligence within a short time for approaching a transaction’s authenticity.
Despite industry advancements in leveraging analytics, there are still industries that need improvement. According to Nic Smith, VP of analytics product marketing, SAP, analytics in marketing is not yet table stakes for several industries like financial services, high-tech, manufacturing, and telecommunications. “A lot of valuable insight into customer behavior is lost when companies do not have a robust marketing analytics system,” he adds.
Marketing has completely transformed, especially for business to business (B2B) businesses, and as a result, insight is critical. For example, Smith believes many high-tech companies run social media campaigns, but don’t really know if it has an impact on the bottom line. B2B companies may question if customers bought database products due to social media campaigns or other influences. Without the proper tools and technology, businesses are left with several questions and no answers.
From an adoption standpoint, Vijay Anand, senior director, product marketing, MicroStrategy, believes it’s difficult to isolate industries that don’t keep pace with leveraging analytics. While a handful of non-profit and higher education organizations tend to be slower in adopting analytics due to resources, time, and sponsorship, Anand says most industries across the board are nowhere near to realizing the full potential of data and analytics. “Even the most tech-savvy organizations have some work to do before they’re fully digitized. The potential for analytics is being met at a very small scale, especially as it relates to AI and Internet of Things.”
However, as vendors build better technology by making analytics simpler and easier to adopt, schools and academic programs now include analytics as part of their core curriculum training to fill the skill set gap. Anand believes this will ensure that tomorrow’s leaders have the mindset that a data driven strategy is critical to the overall success of an organization. “From the advent of the chief data officer to savvier leadership, we’re seeing strategic changes happening within each and every line of business in every industry.”
Businesses commonly make mistakes that hinder their ability to properly utilize analytics to better understand their market. One common mistake is failing to approach problems with an interdisciplinary tactic. When it comes to properly using analytics to understand their markets, Frank Moreno, VP of worldwide marketing, Datawatch, believes companies need to equip business users who have intimate knowledge of the situation with the tools that allow them to perform complex analysis on their own.
“A company cannot simply throw data scientists at a problem because too often a brilliant data scientist may lack the context or inside knowledge that may explain certain trends, or they don’t think like a marketer,” Moreno reveals.
Additional mistakes occur when analysts are not supplemented with external data sources. Moreno says people usually shy away from supplementing their analysis with external data because it’s hard to access or the format and structure doesn’t match up with the in-house data—blending it all may seem impossible and monumental. “Simple supplementation like blending geo-location data to pinpoint customer locations when planning an event are often neglected because it’s too hard.”
Some organizations build silos of knowledge instead of a collaborative ecosystem that allows for knowledge by sharing. Chris Benham, chief marketing officer, Yellowfin BI, believes understanding cross-organizational insights and dependencies and connecting them centrally is a key component to effective decision making. Without it, a decision that improves one area of marketing may negatively impact a different, more important aspect. “Seeing the whole picture before making a decision is a key component to properly using analytics in marketing.”
Waiting until all the data is in place, cleansed, and integrated before starting on the analytics journey also slows down the process. “The analytics journey should start as soon as you have questions because you can learn from even basic data and a journey that never ends and should never—insights should lead to more questions, which lead to more insights, and so on. The most important thing to do is just begin and see where the data takes you,” says Elissa Fink, CMO, Tableau Software. Once a business starts the analytics journey, it’s important to understand that improving on data insights is never complete.
General Tools for Understanding Market Data
A variety tools and technologies exist that help businesses better understand market data. According to Fink, marketing technology has improved in great strides over the last decade with software applications for nearly every role in the department. “Each of these tools—whether it’s your website content management system, Salesforce, Hootsuite, or an email marketing tool—generates massive amounts of data that can be used across the organization.”
Fink suggests marketing teams utilize a single data analysis tool that pull in data from various sources while still being easy enough for a user without technical expertise. “But, don’t get too erudite about it—virtually every person in the marketing department should be using data even in very basic ways to ask questions about how to improve marketing performance,” she says.
General analytics tools sometimes depend on the user’s skillsets. For simple visualizations and temporal analysis, Santiago Giraldo, director, product marketing, Carto, says business intelligence tools provide user interfaces for working with data. Users that intend to perform more advanced analysis typically use either data science libraries and notebooks or open source analytical software. According to Giraldo, data science libraries and notebooks are typically reserved for trained statisticians and data scientists that work with large amounts of data using advanced methods like machine learning or modeling.
Open source analytical software offers advanced analytics accessible to a wide range of audiences. “This software brings new data streams together with advanced analytics and machine learning for entirely new audiences like business users, developers, and analysts,” explains Giraldo. “While the software creates a new level of accessibility, it is not quite as versatile as pure data science, although often times this work well together.”
According to Lisa Loftis, best practices, SAS, the Marketing Technology Landscape Supergraphic developed annually by Scott Brinker of chiefmartec.com now has over 5,000 vendors and covers six discrete categories of tools that range from traditional marketing technologies like marketing automation and campaign management to analytics applications, data management platforms, and CRM systems. “Picking the tools that are right for a particular organization is dependent on the marketing objective that organization is trying to accomplish.”
Automation reduces the time required to develop campaigns and targeted offers required to generate targeted analytically driven activities, says Loftis. Automation features include the ability to develop/utilize sophisticated predictive analytics and models, access to online and offline data, data management processes, and the ability to design and schedule campaigns for a multitude of communication channels.
Loftis believes marketing optimization algorithms are essential to achieve the one-to-one targeting. “These should balance a multitude of business constraints, complex contact strategies, and multiple delivery channels to achieve specific business objectives.” What-if scenario modeling understands the impact of changing constraints while objectives should be available and easy to execute. “The technology should be turned to handle significant amounts of data and complex calculations in the sub-second response times required to react to micro-moments effectively.”
The secret to advanced analytics is utilizing tools that truly understand market data. Analytics tools effectively segment campaigns and track ROI to help business users, developers, and analysts achieve new data streams and effectively understand their data. SW
May2018, Software Magazine