Bring Your Advanced Analytics Game
You’re in constant competition — against other department heads, market rivals, the odds, and even the clock. You can’t afford to compete with yesterday’s data analytics. You’ve got to bring your "Double A" game, or risk losing out.
That means (a) drawing out the best analytics your team is capable of producing, and (b) leveling up your analysts into citizen data scientists who can perform advanced, self-service analyses without learning to code. Sound like a stretch? Not anymore.
Data on the Horizon: Predictive Analytics
Performance metrics change depending on your line of business, but there’s one constant that’s true of every department. More data has the potential to enable better decisions — for tomorrow as well as today.
That’s why advanced analytics means going beyond traditional summarized reporting, also called descriptive analytics. It also gives your department insight into what’s ahead of you, or predictive analytics, and what to do about it, or prescriptive analytics.
Consider examples such as customer response modeling, demand forecasting, and pricing elasticity analysis. Predictive analytics like these are already at work in markets worldwide, to increase revenue, decrease costs, increase customer satisfaction, decrease risk, and increase efficiency.
“Compared to, say, a decade ago, an impressive number of enterprises are data-driven today,” says a recent survey by the NewVantage Partners advisory. Nearly half of survey respondents said they compete on advanced data analytics. Meanwhile, there’s a real cost for not adopting advanced analytics. Performance gaps between front-runners and laggards will widen, McKinsey notes, with the front-runners benefiting disproportionately — as in, double their cash flow by 2030, while laggards will see an alarming 20% decline.
McKinsey predicts advanced analytics front-runners will double cash flow by 2030.
Laggards will see a 20% decline.
Advanced Metrics Prove Out in Case Studies
Let’s get specific around advanced analytics and drill down on customer analytics as an example of the predictive and prescriptive analytics your team could handle with advanced analytics platforms like Alteryx:
Anticipate customer demand so you can provide the right product at the right time to drive repeat business
Target prospects with similar attributes as your most valued customers
Improve marketing campaign effectiveness and brand loyalty by targeting customers and prospects with personalized messaging and offers
Minimize customer attrition by identifying at‐risk customers and taking the analytically-generated “best next step” to retain them
In one case, a Los Angeles fashion company invested in advanced analytics to improve its understanding of customer shopping behavior — specifically with a new customer segmentation and pricing probability model that delivered a 275% lift in revenue over the prior holiday season. Driving the agenda forward, the company added A/B testing that, within three months, reversed a three-year downward trend of average customer lifetime value. In another case, analysts at a social commerce app/retailer reported a three-fold increase in sales from just one of the predictive analytics models they deployed.
See more compelling use cases specifically for marketing.
You can link advanced analytics to your top-level goals and key performance indicators in any department, including:
- Human Resources: Predict attrition to save at-risk employees, preserve the funds that would be needed to replace them, and improve morale
- Sales: Score a lead’s propensity to buy in order to prioritize sales outreach and close more deals
- Supply Chain: Forecast demand and inventory and make more confident decisions that outpace your competition
Who Has the Skill Set and Technology to Perform Advanced Analytics?
Gone are the days when your team members must always queue up with sales, operations, finance, and other departments to get analytics help from the IT department or your company’s lone data scientist. In fact, Gartner has predicted that the analytics output of business users with self-service analytics will surpass that of professional data scientists in 2019. This includes not only analysts but domain experts as well — in a trend toward the democratization of data and rise of the “citizen data scientist.”
Furthermore, you can increase operational efficiency by empowering business users as well as data analysts with self‐service analytics platforms such as Alteryx, since they can be used code-free to deliver deeper insights in hours, not weeks.
Getting Your Team in the Game
Your team could be doing more advanced analytics, but you’ve got to get them in the game. Here are eight steps to get them equipped, ready, and raring to go.
1. Clear the way
The step from descriptive to predictive analytics can be a big one for some members of your team. For one thing, they are often overloaded with requests for historical reports. Their tools may not be up to the task — and may even be adding to their current workload of finding, cleansing, prepping, and blending data. You can use self-service APA Platforms such as Alteryx to free your team for higher-level analytics as they minimize repetitive manual data labor.
2. Build confidence
Other members of your team might feel that they’re not up to the task because they don’t know how to write code or lack other specific skills. Help them overcome their doubts, knowing that platforms such as Alteryx are code-free as well as code-friendly, with drag-and-drop tools that make applying predictive analytics to data sets simple and reliable.
3. Identify champions
Identify your team’s ambitious “data champions” and encourage them to expand their horizons — both for the department and for their own career development. Encourage others, who have never thought of themselves using these tools and platforms, to join in the democratization of data and get their feet wet tackling some business problems of their own.
4. Encourage education
Promote continuing education for data analysts and data champions, whether formal or informal, accredited or not. One opportunity is Massive Open Online Courses (MOOCs) on predictive analytics, such as the Udacity predictive analytics nanodegree co-developed by Alteryx.
5. Reward experimentation
Urge your team to experiment with free trials of self-service platforms such as Alteryx, and report back the results. Make sure they explore the library of starter kits, or analytic templates, that can kick-start the fun.
6. Facilitate partnerships
Suggest player-coach relationships between your team members and the data scientists in your organization’s center of excellence. Or set up informal “lunch and learn” gatherings to exchange information. After all, you know the business, and analysts know the metrics. Together you bring that “Double A” game.
7. Prioritize socializing
Encourage your data champions to attend seminars and conferences, including Alteryx Inspire, to connect with other like-minded analysts and data scientists and share brilliant ideas.
8. Give meaning
Paint your team a fuller picture of how they’re contributing to the larger goals of your department and the company as a whole, keeping the work meaningful and impactful.
As a business leader, you’re in a position to put advanced analytics to work — enhancing your ability to do what’s right for your company, make confident decisions, and get ahead of the competition. But it’s up to you to rally your team and draw out its best analytics work. The good news, as Deloitte has noted, is that “technologies to enable transformation are not only getting more powerful but also more readily accessible, easily implementable, and economical than before.”
With your support and encouragement, your team will return the favor a thousand-fold.
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