Accountability. Marketing dashboards provide metrics you need to effectively manage your brand and optimize performance.
As economic times strained the purse strings in organizations, you hear the word accountability bandied around more and more. But, accountability was never a strong suit among marketers due to the poor metrics available to assess performance. Marketers either dealt with making performance assumptions based on trend analysis or relied on metrics that didn’t bear a strong correlation with success, such as increased awareness. Marketers often lacked the skills necessary for in-depth performance analysis, even if they had the data available, as you can see below from an evaluation of the biggest skill gaps in the marketing discipline.
Among the skills I focus on teaching in my analytics class taught as part of the digital marketing certificate program at Shenandoah University is creating interactive marketing dashboards containing appropriate data visualizations to aid communication and decision making to improve performance. We work primarily with Google Analytics, as it’s a free platform, while many proprietary software programs provide similar insights on social media performance across 1 or more platforms, often at a steep price. A well-crafted dashboard that adjusts to the nuanced information needs of individual users by filtering by product, time period, channel, etc, easily communicates the state of a firm’s marketing programs and highlights where the current strategy falls short of expectations.
Big data/ big headache
Big data isn’t a new thing; it’s the size of data that’s new. In just the last 2 years, we generated a zettabyte of data from scanners, mobile devices, web-connected devices, television, and computers. To put that in perspective, every 2 days we create as much data as we did from the beginning of time until 2008!
And, every 2 years, we create 90% of all the data in existence.
By 2020, experts predict there will be 50X as much data as exists today.
That’s a lot of data.
But, data doesn’t mean much unless you can do something with the data — and here we’re most concerned about using big data to improve market performance.
Unfortunately, the character of big data is that it’s not only BIG, it comes at your extremely fast so unless you have a tool that provides automated insights quickly, you lost all the advantages your might gain from having the data. You need to go from data collection, to analysis, to visualization, to strategy quickly or risk falling behind.
Here’s what Gary King, professor at Harvard University had to say about the big data revolution:
There is a big data revolution. But it is not the quantity of data that is revolutionary. The big data revolution is that now we can do something with the data. The revolution lies in improved statistical and computational methods, not in the exponential growth of storage or even computational capacity
Thus, when we talk about the big data revolution, we’re talking more about interpreting that data — putting it to practical use — than the amount of data generated, which is pretty uninteresting by comparison.
Marketing dashboards as a tool to monitor your success
Marketing dashboards help corral big data to make interpretation easier, faster, and help develop more actionable insights. Basically, marketing dashboards bring together data from various sources into a single page or two so every piece of data needed for decision-making is altogether in one place rather than a decision-maker having to search through various files to find the information he/she needs.
Dashboards created using tools like IBM Cognos or Google Data Studio help bring data together from various sources to aid decision-making. Even Google Analytics provides the opportunity to create dashboards, although this product is limited to evaluations of my website performance. I can even import dashboards created by others to use in evaluating my data. IBM Cognos, however, involves a hefty fee, while the others are free.
Interactive dashboards offer a way to adjust data to fit the needs of the individual decision-maker. For instance, I might manage a single channel or product for a firm. I can use an interactive dashboard to focus on my areas of responsibility without the distraction or cognitive load associated with a static dashboard populated by data covering all channels and all products, which is needed by C-suite decision-makers to guide overall company strategy. I can also adjust time frames, as well so I can evaluate month over month and year over year performance without having to create a new dashboard.
Why use a dashboard
Here are some of the reasons to make the effort needed to create marketing dashboards:
- Data visualizations are a graphic representation of data in a format easily interpretable by managers who need to use the data but may not understand its importance or interpretation if displayed in a tabular format. People are object-oriented and finding a nugget of information in a table full of numbers just doesn’t work for most people. Dashboards bring together salient visualizations to answer business questions, such as how can I increase my conversion rate. Below, you can see a dashboard crafted by a paid solution.
- Note that the dashboard used various types of visualizations as the visualization should match the data type and insights desired from the data. More about this later.
- The information contained in the dashboard is related to achieving one or more marketing objectives. Examples of information that might be displayed are sales, margins, customer satisfaction, market share, Brand development index (BDI), stock-outs, etc. Displaying data that doesn’t contribute to the firm’s success, muddies the waters and offers a distraction for readers that clouds decision making.
- Viewing the results displayed in the dashboard guides future plans so, rather than a firm producing dashboards that never reach decision-makers or are ignored routinely, you need a process whereby decision-makers get relevant dashboards and use them to guide strategy. Failures happen when those receiving the dashboard don’t understand its value or how to use the information effectively. For instance, a decline in sales over time might indicate the need to re-evaluate your current marketing strategy by increasing advertising, targeting messages more closely with the target audience, or reducing the price. Only by analyzing relevant data can a decision-maker know which course of action offers the best solution. Meanwhile, an increase in costs over time might suggest a different marketing strategy building on sourcing issues or re-design of the product with less expensive materials. Multiple graphics on marketing dashboards help managers determine which change in marketing strategy fits with ALL the data supplied.
- Information forming a marketing dashboard includes those reflecting internal records spread across multiple databases and, potentially, multiple functional areas of the firm. For instance, sales might come from the marketing or sales department, while cost information may reside with accounting. Some information likely comes from scanning outside resources like Google Analytics, as well as through market research. For instance, market share relies on knowing the estimated size of the market while BDI requires information on the size of a market segment, both involving outside resources. Developing dashboards from the combination of metrics across different databases require some skills such as Python or SQL, open-source software programs used to combine elements from different databases.
- Information on a marketing dashboard may be transformed using a variety of analysis techniques prior to graphing it. In addition, a firm might use traditional statistical modeling software to perform regression, Logit, or other statistical analysis, then graphically depicts results on a dashboard.
Great data insights don’t mean much if the folks controlling change don’t understand them or don’t have the time to pour over columns of data. Enter data visualization; the key to getting data insights to cause change that improves your market performance.
Data visualization is a little like herding cattle — it’s expensive and time-consuming, but, ultimately, necessary if you want to generate profits from your cows. Filling your marketing dashboards with endless columns and rows of numbers doesn’t aid interpretation as much as a well-constructed visualization.
Of course, data visualization is only 1 means of corralling big data into something useful. Data analysis using statistical tools to generate descriptive, predictive, and prescriptive data analysis also synthesizes meaning from big data.
Even with data analysis, data visualization makes it easier to see not only descriptive data like height, age, and income, but predictive analytics reflecting the relationships among data, and prescriptive data showing the best alternative solutions.
Data visualization tools
Of course, the fall-back position in data visualization is static tools that produce boring line graphs, pie charts, and bar graphs. Even with bright colors, tools like Excel and SPSS produce pretty boring visualizations. A better tool is Tableau, which is the one I teach students in my classes. This tool is specifically designed for data visualization and offers an almost endless supply of different types of visualization so you can easily find the one that fits your needs. Tableau even offers training through their blog to help you make better decisions regarding which visualizations help with different analytics projects.
But, regardless of the tool, data visualization, at its best, should uncover new patterns of relationships not visible to the naked eye. Hence, the key to effective data visualization is the ability to capture patterns and relationships in clean, simple visuals that allow the signal to stand out from the noise contained in the data.
Some tools for data visualization involve automated analysis of data as it comes in, while other tools involve manually creating infographics or other data visualizations that provide insights. Today, we’ll focus on automated tools used to analyze big data.
A subset of these data visualization tools is interactive, meaning individual users can customize the visualization to match their needs. For instance, a brand manager may wish data visualizations of a granular nature to observe nuances within the data, while the VP Marketing may wish data visualizations providing overviews across the various brands, with less granularity. The key to interactive data visualization is the ability of users to expand analysis; allowing the VP to deep dive into a piece of data to see more granularity.
If you’re interested in exploring options for data visualization, check out this post for a good list of available tools.
The problem of unstructured data
Unstructured data — words, images, video, and all other non-numeric data — represent 80% of the data available according to IBM and other digital data experts. But, the tools available for data visualization of unstructured data lag far behind those for visualizing quantitative data because the statistical tools necessary to derive meaning from unstructured data reflect a similar lag.
Tag clouds or word clouds are a rudimentary type of data visualization that transforms words into a graphic reflecting the frequency of word usage.
Association trees depict relationships
among words used. For instance, here’s an association tree from Information Management showing relationships between customer sentiment about a brand:
Cubism Horizongraphs is a tool for analyzing video and audio files. Using this tool to analyze call center interactions or sales presentations offers insights into customer problems, decision factors, and intentions.
The analysis provides data visualization insights from multi-dimensional data from a variety of sources, thus providing a 360 view of a customer or prospect.
Network Graphs, like this one, show semantic relationships from large, contextual datasets. In this graph, you see the relationships between the characters in Les Miserables.
Data visualization for unstructured data: Software
New tools for using unstructured data to create data visualizations crop up all the time. Some of the big boys, SAP, IBM, SAS, and others have tools that adapt to unstructured data. In addition, here are some specific tools for handling unstructured data visualizations:
Datawatch provides a solution that creates visualizations from structured and unstructured data and, most importantly, integrates data types and sources. Options for data visualization include tables and grids, surface plots, stack graphs, and spread graphs. All data visualizations are interactive and they offer a dashboard for integrating across data. They offer a free trial and training but don’t display pricing on their website.
Workday Big Data Analytics provides solutions for data visualization for both structured and unstructured data.
MarkLogic provides a solution, Tableau, for visualizing structured and unstructured data that doesn’t require coding or IT support.
Zoomdata provides data visualization for semi- and unstructured data using tools like Hadoop and NoSQL to create interactive dashboards for visualizing historic or real-time data.
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