O N E T O U G H Q U E S T I O N : D ATA /A N A LY T I C S 2 0 1 6

THE RIGHT DATA CAN DRIVE CUSTOMER ACTION

HOW MARKETERS CAN USE DATA TO SPUR CUSTOMERS TO BUY, ADVOCATE, SHARE,

AND ENGAGE.

Predictive analytics gives

marketers a view to the

possible. Demographics

provide a basic understanding

of customers. Behavior data

suggests customer intent. But

what marketers really need to

know, with certainty, is how

to change or drive customer

behavior. And, for that, not just

any data will do.

Is one type of data best

suited to initiate customer

action, or do marketers have

multiple options to choose

from? Thirteen marketing

insiders divulge which type

of data they assert can spur

customers to action, and

explain why.

— Ginger Conlon, editor-in-chief

TABLE OF CONTENTS

4 Arthur Hall Multichannel consultant, Quad/Graphics

5 Liz Buderus, VP, product management, Epsi-lon; Loretta Jones, VP of marketing, Insightly

7 Joe Pino, director of client insights and strategy, Clutch; Matt Riley, CEO, Swiftype

11 Eric Duerr CMO, Rocket Fuel, Andrew Dennis CEO, NorthPage

6 Kylee Hall, senior director, Leadspace8 Josh Reynolds Head of marketing, Quantifind

10 Victoria Godfrey CMO, Avention

10 Duncan McCall CEO & Cofounder, PlaceIQ

9 Daniel Ziv VP, customer analytics, Verint

9 Susan Bryant CMO, DialogTech

ARTHUR HALLMultichannel consultant, Quad/Graphics@QuadGraphics

In today’s complex marketing environment, establishing links between demo-graphic, social, psychological, and cultural characteristics is more critical than ever before. As individuals become increasingly unpredictable and customers continue to diversify, strategic segmentation can provide the insights needed to improve reach, sales, and return on marketing investment. This qualitative data approach is based on individuals’ needs and expectations and on the reasons underlying their behavior rather than specifically on what they buy or what they do. Marketers that uncover an in-depth understanding of consum-ers while identifying the factors driving behavioral patterns are equipped to expertly influence creative design and copywriting, which increases relevancy and improves campaign performance.

Consider a retailer that analyzed customer data and built affinity groups based on product purchases. In one group, a head of the household (woman A) buys work clothes for men on occasion and frequently purchases women’s nursing apparel; another head of household in that group (woman B) looks similar based on past purchases. Using data that is too basic may depict false identical characteristics between these two consumers, or inaccurately ex-ploit certain shared qualities. For example, woman A does not want to spend time researching products; she wants the best deal and she wants to make a quick purchase. However, woman B is more interested in learning about prod-ucts, perhaps knowing how it was produced or its environmental impact.

With an attitudinal and behavioral marketing view, the retailer was able to identify the types of messages and information that would drive consum-er response. That view also provided insight into the most effective direct marketing format for personalized, timely engagement. Woman A, who was inter ested in pricing and general product information, benefited from an email approach that was less expensive than producing and delivering a mail piece. Woman B, who desired to consume information before making a purchase, received an innovative print format that provided the necessary space for product features and benefits.

As a result of understanding what imagery, copy, and mechanism would influence each woman’s buying decisions, the retailer generated more than $9 for every $1 spent on the marketing campaign, nearly a third greater than the average high-performing sell-over-marketing investment.

LIZ BUDERUS VP, product management, Epsilon @EpsilonMktgAs human beings, we’re drawn to personal relationships. We come into these relationships because in spending time together we develop connections. Over time we learn more about these people, relate to them, and develop deeper connections.

Today big data allows marketers to create connections with customers the same way: by listening, responding, and growing together. To do this effectively, marketers need to leverage data appropriately, dependent on the stage of the relationship with a customer. Delivering personalized experiences is something that should only be developed

based on mutual trust between a brand and the customer it’s trying to reach. At the beginning of every relationship you need to spend more time asking questions

and listening than on responding. During this stage in marketing, leverage general demo-graphics about your customers: where they’re located, age, gender, etc. Match all of this data to what they’ve purchased from you to help round out your understanding of them.

This approach is best done for newly acquired customers about whom you know little. Once you’ve listened, it’s time to learn more about your customers and understand

their needs. Compare different groups of customers based on their customer profiles (for instance, dress buyers versus new product announcement buyers) using demo-graphics, general personas, interests, financial information, and competitor spending to paint a comprehensive image of each group to truly understand their differences.

This is the stage during which you can entice action by using data to deliver relevant one-to-one messages.

Now it’s time to make the relationship more meaningful and show your customers you understand them better than they understand themselves. Entice action by using data to deliver marketing messages around specific needs you know each customer has (or will have). As a guide, look at companies like healthy living website Lifescript, which saw a 30% lift in conversions after implementing targeted messaging.

The most effective way to use data is to create customer personas that take your first-party data (purchase, browsing, opening, clicking, device, and more), demograph-ic data, and third-party spending data into account. These personas will help explain the different types of customer relationships you have.

Once done, focus on driving home the imagery, the words, and the products and services for each group so they will say, “You get me, and I get you.”

LORETTA JONES VP, marketing, Insightly @insightlyapp

Companies that use predictive analytics

can easily anticipate customer needs, al-lowing those businesses to take a proac-tive approach to customer service rather than reacting to problems as they arise.

In addition to existing internal customer data, predictive analytics vendors bring in external data, such as social profiles, fund- ing press releases, and data on sites like TechCrunch, to give users more accurate buying signals from prospects and churn signals from existing customers. Having this data accessible can help businesses identify customer behavior patterns that will let them make recommendations for services, products, or upgrades.

At Insightly, we use a customer suc-cess application to determine the health score of our customers, their use of the product, and their likelihood to either purchase more licenses or upgrade to a higher plan. We’re also exper imenting with a predictive analytics appli cation to determine the likelihood of custom-ers on either free accounts or on 14-day trials becoming paying customers. These appli cations allow us to proactively en-gage with those customers to expand their use of Insightly or prevent the loss of that customer.

KYLEE HALL Senior director, Leadspace @kyleehallb @leadspace Every B2B vendor is trying to deliver the right message to the right person at the right time. B2B marketers can use predictive analytics to help. Predictive analytics enables marketers to analyze behavioral, demographic, and firmo-graphic data to find, score, and segment relevant time-specific buying signals. This is a huge step forward for B2B demand generation compared to list buying and batch-and-blast marketing.

But arguably more important is the quality, accuracy, and completeness of the data that marketers use for their predictive analyses. It doesn’t do any good to identify someone ready to buy your product if you can’t find him or her. Nor is it good to dump leads into your CRM system if they’re out-of-date, inaccurate, or missing key attributes needed, for instance, to match a lead to an existing account already in your database.

Consequently, the best type of data to drive customer behavior is real-time data, continuously updated, segmented, and scored with superior predictive analytics to match your ideal customer and put into action in your CRM or marketing automation systems. It’s not a simple answer, but it’s not a simple problem. If there is a weak link in the chain, everything falls apart.

One good example of how it all works together is ObservePoint, a B2B tech company offering QA tools for the Web. Its SDR team used a predictive ana-lytics platform to discover Net-new leads that best match the company’s ideal customer profile, based on firmographic and demographic data aggregated from multiple sources, combined with signals gleaned from the open and social Web. The immediate result was a significant increase in the flow of qualified leads and a boost in conversions.

ObservePoint saw dramatic and measurable improvements in its demand- generation process:

• 30% increase in SDR productivity• 40% decrease in contact acquisition research time• 23% increase in connection rate• 15% increase in Net-new pipe• 8% increase in deals closed• 5% increase in revenue

Based on those results, Doug Jensen, VP of sales at ObservePoint, calculated the ROI of predictive analytics at 640%.

MATT RILEY CEO, Swiftype @matthewriley @SwiftypeOne of the most important stats mar-keters fail to utilize is the type and number of queries that users are typ-ing into your site search box. Defined as “intent data,” users are explicitly

telling companies what they’re looking for. As the num-ber of meaningful queries increase, marketers can more accurately forecast conversions and revenue. For exam-ple, BulbAmerica, which is the largest e-commerce store for lightbulbs and fixtures in North America, uses site search analytics to predict what people are going to buy. They found that users are 4.6 times more likely to convert after beginning with a site search experience.

After seeing the impact that search data has had on conversions and purchase values, BulbAmerica, which has featured the search box more prominently, can now better predict how much inventory is needed at any given time. Because the BulbAmerica team can more accurately predict what people are searching for, it has also implemented a few optimization techniques that have positively impacted growth.

Now the BulbAmerica team can customize certain re-sults to be sure that top-selling or higher-margin prod-ucts are pushed to the top of the results page. The team also deletes results that don’t convert as well. And it can create custom result sets for queries that return no results, proactively guarding against blind spots where users might hit dead ends. These proactive steps bore substantial fruit: Users now spend 12.3% more per order on the BulbAmerica site.

If you’re marketing team is trying to identify low-hang-ing fruit for driving customer action, look no further than site search analytics. They’re a gold mine of information and can help you predict and forecast results while giv-ing you an opportunity to optimize your site in real time.

JOE PINO Director of client insights and strategy, Clutch @ClutchSuccessIt’s no longer a best practice to personalize based on behavioral data; it’s a customer expectation. Today nearly 80% of customers expect their experiences and engagements to be personalized based on their pre-vious behaviors. Businesses that are not delivering a

data-driven brand experience are already falling behind; those that do are realizing tremendous benefits in the form of higher response rate, purchase frequency, positive brand perception, and more.

Understanding consumer behavior across every touchpoint is para-mount in effectively motivating customer engagement and sales. From point-of-sale systems and e-commerce platforms to mobile applications and social accounts, customers are continuously interacting with brands and leaving behind digital footprints that can provide insights on their preferences and tendencies. Leveraging that data means having the abil-ity to more accurately serve up engagements to the right audiences, in the right channel, at the right time.

Behavioral data empowers businesses to understand and align customer preferences with brand experiences. While synthesizing cross-channel in-formation can be challenging, data-driven insights provide opportunities for increased responses and returns. Marketers who successfully analyze behavioral data can understand individual preferences and tendencies through segmentation and scoring. Marketers can use this intelligence to deliver offers and communications that mirror each customer’s interests and behaviors, resulting in higher levels of engagement and brand loyalty.

Crabtree & Evelyn put the power of behavioral data to the test in No-vember 2015 by creating a cross-channel campaign spanning email and direct mail. As part of the campaign, it customized luxury personal-care products based on preference dimensions that included product type and specific product purchases. Dynamic email campaigns that fea-tured personalized content based on established preferences were then tested against a generic one-size-fits-all campaign.

The personalized campaign elicited positive responses and generat-ed double the response rate of the generic offer with an average order value increase of more than 15%. The personalized direct mail campaign also resulted in an increased response of 20% over the generic offer.

JOSH REYNOLDSHead of marketing, Quantifind @josh_quantifind @quantifind

Marketers today need to understand and improve their impact on revenue. Data that reveals why certain market-ing strategies do or don’t work, as well as how to adjust them to improve their outcomes, is essential to marketers.

A well-known QSR brand wanted to understand how to improve its morning sales, particularly among teens. The brand’s breakfast menu included several options aimed at teens and all of the data that had been collected indicat-ed that young people loved the brand’s content marketing. Yet breakfast sales weren’t improving.

The QSR brand aimed to solve this mystery by combining online consumer data with financial data from in-store re-ceipts. However, neither traditional social listening nor pre-dictive analytics tools had been able to leverage this data to reveal why breakfast results continued to disappoint.

Using an on-demand insights platform, the QSR brand filtered out all the spam and other social noise that didn’t represent real people, then filtered again to isolate only the consumer conversations and topics that correlated with the brand’s sales successes. This process revealed a strongly negative correlation between the QSR brand’s breakfast sales and moms’ online conversations about its coffee. Through further investigation, the data began to tell a story: Teens wanted the brand’s breakfast but relied on moms for rides to the drive-through — but because moms didn’t like its coffee, the teens had to choose somewhere else for breakfast. So, to unlock the untapped breakfast sales potential among teens, the brand would have to offer moms better coffee.

This provided the answer that the brand needed. It accel-erated the launch of a new coffee product, and both cof-fee sales and breakfast sales grew significantly, with coffee sales doubling.

SUSAN BRYANT CMO, DialogTech @DialogTechOne of the most important types of customer inter actions driven from digital marketing is often ignored: phone calls. In today’s mobile-first world, digital marketing drives phone calls, and those calls drive revenue. Because of smartphones and click-to-call, this idea, which may have seemed crazy to marketers as recently as two years ago, is well understood today.

Consumers today live their lives on their smartphones, and marketers are allocating more of their digital advertising budgets to target them. According to eMarketer, 62.6% of digital ad spending in the U.S. this year will target smartphones and mobile devices. Thanks to click-to-call, these ads will drive more than 108 billion customer calls to businesses, and those calls will influence more than $1 trillion in consumer spending. That’s why call attribution — knowing how effectively digital marketing programs generate calls, sales pipeline, and revenue — is the next critical step to optimizing digital marketing performance and spend.

Marketers are getting better at analyzing attribution values for their digital channels. But they’re still missing an important part of the cus-tomer journey: knowing the marketing channels driving their calls (such as search, social, display), what search keywords the person used (if they came from search), the ad they clicked on, and the caller’s path through a website before and after calling. Marketers also need data on the customer and the call — who they are, geographic location, time and day of the call, how long the call lasted, and whether it converted to a sale. This data is important in optimizing campaigns and spend.

For example, HotelsCorp uses customer call attribution software with paid search campaigns to track callers back to the search engine, key-words, ad, and landing page that drove the call. The vacation provider also analyzes callers’ geographic location and the times and days that generate the most calls. By using call attribution technology, Hotels Corp can optimize bids for the keywords, locations, and times of day that drive the most — and best — calls. By using it HotelsCorp generated 83% more calls and 71% more bookings while decreasing cost-per-conversion by 10%.

Getting consumers to call is one of the most powerful ways marketers can impact customer acquisition. Call attribution data provides the insight marketers need to accomplish it.

DANIEL ZIV VP, customer analytics, Verint @VerintStructured data is convenient for track-ing metrics and KPIs across an enter-prise, but it sometimes falls short in explaining customer behaviors and driving action. Taking a look at unstruc-tured data can provide a better plat-form for influencing customers.

Mining unstructured customer interactions (such as phone, email, and chat) that are linked to specific outcomes holds the key to influencing customer action. Customers tend to be more detailed and open when speaking with another per-son, so these interactions produce a richer source of data and more actionable insights.

For example, an international bank wanted to better under-stand what drives customer purchase behaviors. Analysis of transaction data indicated that 15.1% of upsell attempts ended in a successful purchase, but the factors driving customers to accept or reject offers wasn’t clear. Using speech analytics, the bank analyzed the actual words and phrases used in the calls that ended with a sale, comparing them with the phrases mentioned in unsuccessful calls. It discovered that asking for “another moment of your time” before the upsell offer was actually driving a negative customer response. Only 6.3% of calls that included this phrase ended in a successful sale.

This analysis also surfaced phrases that were driving posi-tive customer response, such as, “You could be earning X dol-lars in interest on the balance in your account; would you like me to set that up for you?” Calls using this alternate phrasing earlier in the call resulted in 57.6% sales success rate, which almost quadrupled the average customer acceptance rate.

Unstructured data is not only richer but also more readily available. An estimated 90% of the digital universe is in un-structured formats (audio, text, images, and video). The anal-ysis tools for mining unstructured data are significantly more accurate and effective than they were just a few years ago, opening up a new world of possible actionable intelligence.

VICTORIA GODFREY CMO, Avention @aventionCombining various data sources to create a holistic, single view of custom-ers and prospects can help B2B marketers and salespeople better identify those who are predisposed to make a purchase and then focus their efforts on them. By first gathering a variety of data sets and then applying predic-tive models, marketers can identify the characteristics of customers that have converted in the past and then use that data to target similar profiles of customers likely to buy in the future. These signals lead marketers to understand those business behaviors that are linked to the prospects most likely to buy, allowing them to target companies exhibiting those behaviors with messaging and content tailored to those attributes.

For many B2B marketers, this starts with aggregating basic firmographic variables — such as industry, revenue, and number of employees — to pro-vide sales with a better view of prospects and an indication of their likeli-hood to purchase. Breaking this data down further by looking at triggers such as whether a com pany has received recent funding, launched a new product, or is hiring can provide even deeper insights into the right timing and message to close a sale.

For example, the Employee Benefits Association, a health and life in-surance agency, used to take five days to manually research potential

contacts. In one case it created a list of 170 contacts only to reach out to 40 prospects and make no progress even after following up. By using outsourced business insights services, the organization could be more targeted in identifying the most qualified prospects that are also most likely to convert, based on predictive indicators built on business signals and ideal customer profiles.

As a result, the organization focused its efforts on those high-potential prospects, narrowing its target list to 46 companies, which yielded a 26% response rate and seven in-person meetings, including one that converted into a new client.

DUNCAN MCCALL CEO and Cofounder, PlaceIQ @dunkmac @PlaceIQGetting customers to engage with brands first requires a deep understanding of consumer behavior. Even the best of data sources when used in a vacuum only provide a fraction of insight about what makes buyers unique. There’s no single data source that can tell you everything. To understand con-sumers, marketers should concentrate on using multiple data sets in unison to bring true customer view better into focus. These include CPG purchase data, automotive ownership data, TV viewership data, and census data. Location data can act as the connective tissue between these multiple data sets to tell brands what’s happening between each marketing touchpoint, the effect each has on driving foot traffic to specific locations, when and where consumers are most likely to engage, and more.

ERIC DUERR CMO, Rocket Fuel @rocketfuelincThe holy grail for digital marketers is reaching the right customer on the right device with the right message. Many advertising companies claim they are able to deliv-er this. But the reality is more complex than the promise: It takes sophisticated technology, leveraging both arti-ficial intelligence and big data, to reliably and accurate-ly predict drivers of performance and consumer action. An effective predictive data set can include more than 11 million demographic, behavioral, and contextual combi-nations of attributes.

Real marketing value resides in using all this data to pin-point moments of influence — the exact point where the person, the device, the context, and the intent come together with the highest probability of lift or con-version. This is driving the next wave of data-driven marketing.

To identify the right moment, marketers need to activate the wealth of first-party data they own and combine it with the attributes that are driv-ing — and are predicted to drive — results. A unified consumer profile com-bining first-, second-, and third-party data provides a complete view of the customer to identify the precise moment to deliver ads while minimizing the wasteful delivery of ads to the wrong devices and people at the wrong time. Marketers can then tap into data generated in every channel simultaneously, which surfaces up to 200% more data than a standard profile. By focusing on moments, brands can improve direct response performance by more than 30% on average, and overall brand campaign reach by more than 50 percent.

Moment-driven marketing affords brands a much higher level of precision than targeting customers across devices, using data to identify the precise moment to deliver ads that will influence customer behavior. Using technolo-gy to score every moment, marketers can learn the predictors that make one ad more appropriate than another in a particular moment. The technology then learns over time which attributes work to achieve a marketer’s goal.

Microsoft, for example, recently used a data-driven campaign to drive qual-ified job applications to its careers website. Using cross-device technology to make better decisions about whom to target and when, Microsoft was able to beat its CPA goals. One key insight discovered during the campaign was that reaching job seekers at the right moment on their mobile delivered much bet-ter conversion. With a cross-device and moment-driven approach, execution, and measurement, brands are in a much better position to influence customer behavior and action.

ANDREW DENNISCEO, NorthPage @andrew_dennis @northpageUsing traditional analytics to under-stand “what happened” with a cam-paign is no longer enough for modern marketers. They now need action-able insights from a new generation of digital intelligence sources to drive

growing volumes of successful customer actions.Building on the measure of what happened, mar-

keters who leverage actionable digital intelligence can determine:

• WHY: Pinpoint the reasons why something hap-pened or continues to happen (good and bad) in live digital marketing programs

• HOW: Prescribe guidance detailing how to fix or improve the currently deployed programs to drive performance and results

• WHO: identify who has leading digital market-ing program capabilities across competitors, near neighbors, and the universe of brands to gain criti-cal market and strategy perspective

This advanced level of intelligence enables mar-keters to focus on developing digital areas of op-portunity rather than finding them.

For example, Sears began using digital intelligence sources to grow online revenue in its appliances business unit. Though Sears’ marketers were well informed on baseline activity through its integrated digital analytics program, they wanted objective in-sight and an action plan to drive increased KPI per-formance. Diagnostic analysis surfaced average or-der value as a major area of opportunity — providing best-in-class examples for guidance on how to drive lift. Sears’ marketers acted on the digital intelligence indicating where and how to optimize their efforts, implemented specific changes, and immediately recognized a 6.7% lift in average order value.