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Marketing’s Battle: How Big Data and Marketing Technology Help Win The Game

Posted on July 9, 2015 by Glenn Gow
moneyball for marketing, marketing objectives, churn rate

 

The best marketers are those who can quickly build a three-dimensional picture of markets and buyers. They’re proactive marketers who can quickly zero in on who, where, when, why, how and under what circumstances customers buy – the buying context – and act on it.

We often call that intuition, instinct, or “gut feel,” and it holds a mystical place in the minds of marketers. It seems like the opposite of hard data, because intuition doesn’t appear to come at the end of a linear analytical process.

Neuroscientists suggest that “intuition” is really the result of unconscious, parallel processing of vast amounts of information constantly washing over us, information we’re not paying direct attention to. “Gut feel” fits together the contextual information we don’t yet know we know into that “flash” of insight or inspiration.

Predictive marketing analytics uses the arsenal of big data to garner insight from the vast ocean of undifferentiated digital data, building that contextual picture of who’s likely to buy, as well as who’s never going to buy, to optimize marketing objectives. It may not have the mystique of “gut feel,” but predictive marketing intelligence trumps intuition when it comes to boosting the bottom line.

Building the foundation for predictive analytics isn’t a cakewalk. But it’s not an uncharted journey either:

  • Unlock the information you already have siloed in disparate systems. You’d be surprised what you already know about your buyers if it was in one, unified presentation.
  • Every interaction provides a rich source of information about buyer behavior. Use it to make your contextual picture of buyer behavior fuller.
  • Invest in IT architecture purpose-built for real-time processing and decision-making: distributed storage and processing, scalable analytical tools, and visual dashboards.
  • Plan campaigns for “dripping” instead of “blasting,” to respond in real-time to new insights and opportunities.
moneymall for marketing, marketing objectives, churn rate

Paychex Sees Bigger Payday with Predictive Marketing
One company that has shown how much impact insight-driven, proactive marketing can have is Rochester, NY-based Paychex; the leading payroll, HR, and retirement benefits outsourcing service for small businesses. Whether it’s acquiring new clients, retaining existing ones, or cross-selling additional products, Paychex leaves nothing to chance.

Paychex has an enviable 44-year history of steady success. In the 1970s it targeted an un-served market for payroll outsourcing, the 90-plus percent of U.S. companies with fewer than 100 employees. Throughout the 1990s, Paychex grew on average 20 percent annually. In 1997 the company marked its fifth consecutive year of 30 percent growth; 2001 was the 11th consecutive year of record revenues and profit.

Then 2008’s recession slammed into Paychex’s market with hurricane force. The number of businesses with 20 to 49 employees – Paychex’s sweet spot – fell 13.5 percent. Although Paychex remained solidly profitable, business was flat in 2009 and declined slightly in 2010.

“We had to become more focused on marketing, and specifically marketing that drove results,” said Paychex Marketing VP, Andrew Childs. “We’ve always relied heavily on referrals from banks, CPAs, and our existing clients. But in the last recession, it became obvious that we needed to be more aggressive about generating leads.”

At the same time, buying patterns were changing as social media multiplied the ways customers and prospects interacted with suppliers. Marketing was no longer about picking the most efficient, cost-effective medium to reach a target audience. Now it was about many touch points, many audiences and tailored offers and messages for each one.

Childs explains how much this changed marketing:

“We need to do a lot of the educating that used to be done by our direct sales force. Marketing is not just creating awareness and positive impressions. We have to take the prospect farther into their buying process.”

Building a Big Data Foundation with the Resources Already in Place
Paychex began its big data evolution by building context around clicks.

By 2011 Paychex’s cost per acquisition (CPA) was rising. At the same time, pinpointing where leads came from had become impossible. Inbound telemarketers dutifully asked callers how they found Paychex, but responses were vague. Many marketers end up relying on last-point attribution, says Childs.

“But all that usually tells you is that everything ends up on the Web.” Paychex thought online forms were driving most leads, but there was no hard data to prove that. To show return on marketing investment, Childs needed to know more. Paychex needed to see the context around that phone call.

Mongoose Metrics call tracking and attribution analytics supplied that context by assigning a unique ID to each event (click) and creating a unique phone number on the page. Simply by knowing what number callers used, Paychex knew which Pay Per Click (PPC) campaign, the keywords, ad group, and ad creative that brought each prospect to Paychex.

After telemarketers add information about prospects’ interests, leads go to Paychex’s Salesforce ExactTarget Marketing Cloud CRM system, which adds demographic and geographic data. Paychex now had insight.
Better Visibility Yields Surprising and Profitable Insights
That insight delivered surprises. Paid search ads drove more leads for Paychex than display advertising, and branded keyword terms led to more phone calls – not online forms as the company had believed. In fact, the number of leads generated by paid search ads was three times higher than Paychex had previously believed.

Using Marin Software Bidding & Optimization tools for keyword bid management, Paychex automated bid management; dropping unproductive keywords, and focusing on the most effective ones.

Another challenge was conversion latency – lag time between clicking on an ad, responding to a call to action, and actually buying. Typically keyword bidding is based on the closest downstream event.

But Paychex’s average time from click to sale is about two months. So a good campaign isn’t going to show conversions in the short term. Instead, Marin’s predictive bidding model combines its algorithms with user input – for example, the number of appointments set within next 24 hours.

As a result, Paychex increased lead volume by 98 percent within the first year, and CPA declined 43 percent. Paychex can justify its paid search ad budget with insight – not intuition.

Purpose-Built Big Data IT Infrastructure is the Foundation
Behind this data-driven marketing is a Big Data IT infrastructure. Paychex started its journey with information about 580,000 client businesses and nine million client employees stored in 60 databases. A lot of key “context” was stored in those databases, but it was impossible to bring it all together to create a coherent picture.

An architecture purpose-built for real-time processing brings it all together. Paychex has been evolving to Apache Hadoop distributed storage and processing architecture, consolidating data from those distributed sources into an IBM Netezza data warehouse appliance

The appliance approach offers a modular, scalable, straightforward tool that’s as hard-wired for business analytics as a toaster is for making toast. The result is that queries that took weeks of laborious combing through multiple databases, now take minutes using Alteryx Analytics Gallery.

Combined with Tableau Software’s Desktop visual dashboards, marketers get aggregate results quickly, plus drill-down visibility, taking the guesswork out of marketing decisions.

Marketing Based on What’s Happening Now, Not What Happened Last Month
Building a contextual picture around customer actions also provides a better basis than past history for predicting which customers are most likely to buy specific products.

This allows marketing to allocate resources more efficiently and conduct “drip” instead of “blast” campaigns, and replace cold calling with focused offers that only go to the best prospects.

“There are only so many emails you can send to a client. Sending them ones that might actually be of interest I do think improves the customer experience (Childs).”

As a result, the company has seen a 50 percent improvement in the sales call-to-appointment ratio.

Paychex has also been able to develop a Paychex Attrition Model that proactively identifies clients that are most likely to leave, arming sales with targeted messages and carefully designed pricing. As a result of anticipating customer needs, Paychex’s churn rate has dropped from 25 percent to under 7 percent. Plus, the company saves money by not offering discounts indiscriminately.

The ultimate measure is the bottom line. In 2013, Paychex beat industry averages for revenue growth, and sales hit a new high of $2.44 billion. At Paychex, insight has truly trumped instinct.

Originally published on SteamFeed

Image: Courtesy of The Mortgage Reel

Glenn Gow

Glenn Gow is an expert in marketing technology, an Advisory Board Member, Author, Speaker, Podcast Host and CEO of Crimson Marketing. Follow me on TwitterLinkedInGoogle+, and Facebook.


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