Data and Personalization in Sales


Data and Personalization in Sales

90% of all the data that existed in the world in 2010 was created after 2008. How is that possible? Because, post-internet, we’re now generating a mind-blowing 2.5 quintillion bytes of brand-new data each day.

Forget the industrial revolution: the data revolution is upon us, and it’s transforming how we work, live, think, and experience the world.

Hence the development of “big data analytics” (BDA).

BDA helps businesses do the following:

  • reveal hidden buying patterns
  • uncover unidentified correlations or relationships
  • discover user preferences
  • glean market trends

All from an inconceivable amount of structured and unstructured data.

Researchers have marveled at BDA as “the fourth paradigm of science” and “the next frontier.”

If the digital economy was a city, data would be the oil that keeps it running.

And like with oil, an entire BDA industry has developed before our eyes. Companies exist solely to mine and extract data. Others refine it, and flow all that precious information into practical applications.

91% of Fortune 1000 companies invested in big data projects in 2014. This is an 85% increase from the previous year.

That same survey found that investing in data mining and analysis could contribute to 10% or higher growth in revenues for most businesses.

Businesses are spending at least $36 billion a year on efforts to capture and leverage customer data, says a 2017 report.

In the words of Google chief economist Hal Varian, “statistics will be the sexy job of the next decade.”

Businesses are hoping to put those streams of data to work for all sorts of practical applications. But data gathering and analysis is of particular interest to sales and marketing teams.

Harnessing this raw info is like a sales and marketing superpower. Businesses can better understand how consumers feel about their products and services.

Analytics of this size help marketing professionals create personalized services and promotions. Each piece of content, email, or touchpoint is custom-tailored to a particular buying persona.

Data and personalization: you can’t have the latter without the former. And this special friendship is one of the most lucrative aspects to BDA.

Personalization can increase sales by more than 10%. It can also generate an ROI of five to eight times on marketing expenditures.

Tech giants like Amazon and Netflix know this already, of course. They’ve been leading the charge in offering products that make you feel like they’re reading your mind.

They, of course, have more access to big data and more resources to leverage it than about any other company. So what about the rest of us?

Here’s the thing: data and personalization aren’t just for the giants. Any sales and marketing team that’s not already headed in that direction better get on course or risk being left behind.

Ready to learn more? In this post, we’ll cover:

  • A brief overview of data in sales and marketing
  • A concise primer on personalization
  • A cautionary note about the potential perils of the data era
  • And a practical discussion on how AI can help with data and personalization operations

What if I don’t have any data to begin with?

Without insight into your sellers’ actions and your buyers responses, it be be quite impossible to understand your buyers specific interests. Personalization efforts will fall flat if they are not specific, relevant, and satisfactory to the customers inquiries. If visibility into sales situations and truly knowing your buyer is something your team struggles with, check out our upcoming webinar.  We’ll discuss the data sources you need access to, the metrics you should be tracking, and the different ways you can leverage those insights.


The data revolution in sales and marketing: A brief history

Applying data-driven statistics to sales and marketing didn’t suddenly emerge with the advent of Google. In fact, this is a field of inquiry that began over a century ago.

It all started in 1910 when Charles Coolidge Parlin began gathering market data to guide advertising and other business decisions at his firm, the Curtis Publishing Company..7

Parlin’s pioneering work was so successful that a suite of companies across the country followed suit. Commercial research departments began to bubble up as far as the eye could see.

Though it was steadily developing, the field didn’t innovate again until the first big technological disruption came in 1972.

That year, retailers were able to capture data for the first time. IBM’s new technology, the Universal Product Code, made this possible. They were also the first to put in place electronic point-of-sale devices.

A decade later, IBM’s first personal computer became available for everyday households to buy in 1981.

As sales reps started to work on their own desktop units, a mini-revolution in the use of internal customer data in sales and marketing was fomented.

This culminated in the development of database marketing, which arrived on the scene in 1987.

From here on out, things start to develop at a fast and furious pace. The first Customer Relationship Management (CRM) software became available in 1990.

Five years later, the internet would change reality as we knew it for good.

Since then, companies are only improving at tracking everything we do on a computer or internet-enabled device.

Today’s sales and marketing professionals thus have unprecedented access to more data than ever before.

Data-driven recommendations are available in real-time at a low cost. Sales reps have many profound and granular insights into consumer behavior.

The challenge today is figuring out how to harness, analyze, and leverage all that data. Standard statistical formulas developed over the past century can’t handle these large volumes of data.

That’s why so many firms are now turning to the power of artificial intelligence (AI) to extract the full information value of that data.

And that brings us up to today in this brief history: AI most likely marks the next major disruption in the data revolution. (More about using AI to leverage your data soon.)

As mentioned above, one of the most significant benefits BDA offers is personalization in marketing. Let’s take a look at what that means next.

SEE ALSO: A Definitive Guide to Relationship Intelligence


What’s personalization got to do with it?

“Personalization” in sales refers to customizing services, messaging, and offerings to the sales target’s unique needs.

Think of personalization as the logical continuation of “adaptive selling.”

Adaptive selling has proved itself as the most effective form of selling. It also involves adapting one’s mannerisms, the content of one’s presentation, one’s form of address, etc. to the customer.

Personalization takes that general concept and turns it into a data-driven science.

Personalization involves the following three steps:

  1. Analyzing data-driven statistics to learn about your buyer’s behaviors, feelings, and preferences.
  2. Adapting your offerings based on that acquired knowledge.
  3. Evaluating how effective the personalization was based on the buyers’ engagement. That knowledge, in turn, is then put into play in the next round of personalization.

Sounds straightforward, right? Let’s dig a little deeper into the practice to get a better idea of how it works.

The three methods of personalization

There are three core personalization methods used by sales and marketing teams:

Pull personalization

This involves the sales rep or business providing a personalized service when the customer asks for it. The customer is in charge here, while the business is relatively passive.

Think, for example, if the buyer asked for a sales presentation to be provided as a YouTube video, so that they could watch it at home on their iPad. That’s a customized offering, but one the sales target demanded.

Passive personalization

This involves the business taking the first step in making a personalized offer based on customer behavior data for a product or service.

But here, the offer in question can only be taken advantage of if the customer takes action. This includes things like having to go to the store to redeem a loyalty coupon. Another example would be choosing a movie to watch based on a Netflix recommendation.

Push personalization

This is the inversion of “pull personalization,” in that the business now takes charge and the customer is wholly passive. Push personalization involves sending a personalized product or service to the customer without them asking for it.

Think of a Pandora playlist, for instance. Pandora is providing music that the customer didn’t have to ask for, or even think about. They just listen to it (if occasionally providing feedback on how much they like each track.)

The three levels of personalization

Personalization exists on a spectrum. From the broadly, generally personalized offering to the thoroughly individualized offering. Here’s a breakdown of the three levels of personalization:

Mass personalization

This is when the entire audience receives the same service, marketing materials, offering, or what have you.

Let’s think of an email campaign, for instance. Email messing is often personalized to the level of the “average” taste of an audience segment.

Segment-level personalization

This is when you divide your audience into different segments based on distinct preferences.

In this case, your offering, marketing mix, etc. is personalized to meet the needs of that segment or category. Here, you might have three different variations on the email campaign we mentioned above.

Individual-level personalization

This is when the customer receives offerings, services, and marketing materials customized to their particular taste, behavior, and feelings.

This is the most time- and cost-intensive level of personalization. Also, beware: it’s not always desirable to personalize to this deep of a level. Care must be taken not to make the customer feel that their privacy has been invaded in any way.

Examples of customized marketing

Let’s take a look at a few basic, real-life examples of personalization in marketing, and its impact on revenue.

Personalization example 1: Bloomspot

Bloomspot analyzed customer credit card data to identify its most loyal customers. They then offered them rewards (such as follow-up offers and benefits) based on their spending records. The company reported the program increased customer loyalty.

Personalization example 2: launched a personalized email marketing campaign. They tailored different email campaigns to the preferences and tastes of their audience segments. reported a marked increase in sales in response.

Personalization example 3: sends each customer personalized offerings. They base their messaging on customer browsing patterns, login counts, and previous purchases. says that the practice prompted a 133% increase in sales and a 200% increase in user engagement with the website.

Consumer personalization trends: Looking toward the future

A major personalization trend right now in sales is developing adaptive personalization systems.

In “closed-loop marketing” (CLM), sales and marketing teams use data-driven insights to build campaigns. Then sales reports back to marketing about how the leads they received from those campaigns performed?

Adaptive personalization involves automating that CLM feedback loop into a continuous cycle.

Adaptive personalization allows sales and marketing teams to provide customized services in real-time. (Think of the by-the-hour personalized deals offered by Groupon, for instance.)

The following factors will speed up the development of adaptive personalization systems:

The growth of the so-called “internet of things” (IOT). IOT refers to a network of physical objects equipped with sensors or software that can communicate with each other over the internet.

Think of how your Alexa can start your washing machine, for instance, or how your smart fridge can reorder something from Amazon.

Data can increasingly be gleaned from this internet of things to optimize personalization.

The expansion of “natural user interfaces.” Consumers interact with these through voice, gaze, facial expression, and motion control.

The more consumers use these devices, the more possible it will become to automate attention analysis at a mass scale.

SEE ALSO: How to build Meaningful Connections with Your Clients


The increased popularity of “wearable tech.” These include Apple watches and Google glasses. Wearable tech will also generate new and more types of personal data that can further adaptive selling practices.

The dark side of data in sales: Balancing personalization and privacy

So far, we’ve focused on the more utopian dimensions of the data revolution, especially the possibilities it has opened up to businesses. But it’s crucial to also address the revolution’s dark side.

As today’s customers generate massive amounts of trackable data, they’re also anxious about their privacy. Data can certainly be misused.

Simply knowing that a business could access their personal data can trigger customers to feel violated and to lose trust in the business.

One 2017 survey found that if they were told that a firm had accessed their personal data, 22% of customers would take their business elsewhere.

Further, 23% would be more likely to spread negative word-of-mouth about those businesses. Finally, 10% would be more likely to falsify any personal information the business asked them to provide.

The study found these customer perceptions hold true regardless of industry or demographic.

More to the point, over 75% of consumers are uncomfortable with the amount of information internet marketers have about them.

As valuable as this customer data is, it is imperative that sales and marketing teams make a good-faith effort to respect customer privacy.

If they want any chance of building a trusting relationship with them, that is.

Fundamental best practices here include abiding by all applicable laws on data mining privacy.

But there are also proactive measures businesses can take to make their trustworthiness more clear.

For instance, a 2014 study found that websites that gave users more control over their personal information (such as an easy way to opt out, etc.) had a clickthrough rate on personalized ads that was twice as much as competing websites.

Here’s the point: the more you can find a way to show your buyer you respect their privacy, the better they’ll feel about engaging with you. (And potentially sharing their information in the future.)

SEE ALSO: What You Need To Know About GDPR


How AI can leverage your data to optimize personalization efforts

So far, we’ve looked at how important personalization is in sales and marketing activities.

We’ve seen some examples of data-driven marketing, and also taken a moment to mull over the negative ramifications of the big-data age.

Now that we’ve got a handle on the big picture, let’s turn our attention to the nitty-gritty. How are you going to collect and analyze all that data that you need to optimize your personalization efforts?

Most businesses rely on sales force automation (SFA) systems for that. There’s a range of software designed to automatically capture customer data and analyze it for various applications.

These systems might help with personalization, for instance. But they’re also meant to generate more accurate sales forecasting, inventory control, and more.

Businesses invest millions of dollars a year in these SFA systems.

So the fact that 61% of these systems reportedly fail to fulfill their purpose is a serious problem.

The two main reasons that sales teams don’t like or can’t use their SFA systems include:

  • The system’s lack of storage and analysis capabilities
  • Insufficient input data

Remember how we said above that AI was the next seismic disruption in the data revolution? Well, this is where it comes into play.

The next generation of SFA platforms rely on AI’s immense capacities to create powerful systems that now process huge volumes of data to get actionable insights.

Accent Technology’s AI-driven Marketing Insight tool can show teams what content is being shared by prospects (even through email attachments). This gives sales reps 100% visibility into all content shared with customers.

The AI algorithms scour the buyer engagement data to surface granular insights into content performance.

Thanks to its automatic activity capture, the Marketing Insight product even sheds light on the key topics inside the content, and how specific buyer personas respond to them.

All of this information can then be used to personalize and optimize future sales content. And AI keeps playing a role here, too.

For instance, Accent Connect, an AI-driven content management solution, offers one-click personalization, allowing sales reps to deliver tailored, personalized materials quickly and easily to buyers.

For more information on how Accent can help you optimize your sales calls (and your entire sales strategy), contact our team today.

Accent Technologies is the first and only SaaS company to bring together Sales AI and Content Management in a true Revenue Enablement Platform. We provide both sales and marketing with better visibility into the performance of their teams.

This drives revenue through intelligent recommendations for complex sales scenarios and provides the data for rich analytics that power better coaching, forecasting, and long-term customer support. Learn more about our solutions or request a live demo to see it in action.

By Accent Technologies

18th January 2021

Data and Personalization the Accent Way

Many sales and marketing leaders struggle to understand where to employ AI to improve their business. But waiting could be costly. As a matter of fact, 71% of marketing executives plan to deploy AI in 2020.

Forward-thinking revenue teams are using AI to gain visibility beyond marketing automation and into buyer activity and intent throughout the selling cycle. With this insight, AI-powered marketing teams have an incredible competitive advantage.

Contact us today to learn how how to use AI to truly know your buyer and drive revenue. Don’t get left behind!