How Data Science Can Help Your Enterprise Generate More Revenue


Data science is a dry term for a surprisingly cool field. When used right it acts like a team of digital detectives, sifting through a company’s data to ferret out inefficiencies and spot opportunities in time to act.

“Used right” is the key phrase here. Data science is a complex field, and finding a path to revenue presents a challenge for companies trying to modernize their digital strategy. Sometimes it’s hard to see past the hype to the actual business value of investing in data science.

To help cut through the noise, here’s a clear, results-focused look at exactly how data science generates revenue for enterprise.

Laser-focused marketing campaigns

When it comes to marketing campaigns, there’s no such thing as too much data. Over 80% of senior executives want detailed analysis of every campaign, but they often lack the time or data to gain real insight into campaign performance.

Source: Concepta, Inc

Data science addresses both those concerns. Artificial intelligence and machine learning methods cut down on the time necessary to process data while better data management provides the fuel to feel analytics.

The right combination of data science techniques can help track how campaigns are doing by market and by demographic within that market. This includes information as general as click-through rates to sorting the time spent on a company’s page by the originating site.

Armed with this information, marketers can refine the ads they push to each market based on what works, not what should work based on broad demographics. They can even identify customers who failed to convert late in the process. About 70% of these customers will convert after being retargeted.

The results are impressive. Using data to guide marketing campaigns leads to a 6% increase in profitability over companies that are reluctant to adopt data science.

Better e-mail follow-through

E-mail optimization is probably the most direct example of data science driving revenue.

E-mail is a major source of revenue for enterprise, especially for B2B companies and those that focus on e-commerce. A full 86% of professionals prefer to use e-mail for business correspondence.

The same percentage are happy to receive e-mail from their favorite businesses (providing it doesn’t get excessive).

More than half of CMOs say increasing engagement is their main concern about e-mail marketing this year, but three quarters of them don’t track what happens after e-mails are sent.

Only 23% use data science tools to track e-mail activity. A mere 4% use layered targeting, and 42% use no targeting at all. (Four out of five do perform at least some customer segmentation, though.)

This oversight has a serious effect on the bottom line. 51% of marketers say a lack of quality data is holding their e-mail campaigns back. Without data to guide them, they struggle to evaluate customer satisfaction with the frequency and quality of the company’s e-mails.

Increasing e-mail quality using data science has measurable benefits. When customers make a purchase through links in an e-mail they spend about 38% more than other customers.

80% of retail leaders list e-mail newsletters as the most effective tool in keeping customer retention rates high.

On a smaller scale, personalizing e-mail subject lines increases the open rate by 5%. Triggered messages such as abandoned cart e-mails have an astounding 41% open rate (and remember that 70% retargeting conversion rate from earlier).

Lead management

Sales staff only have so much time, and analog lead assessment methods yield questionable results. Artificial intelligence-powered data science tools can analyze a company’s past sales and customer data to effectively score leads, letting sales staff make the most of their business days. These tools consider factors like:

  • Actual interest in product as demonstrated by events like site visits and social media discussion
  • Position in purchase cycle based on time spent on specific areas of a website
  • Demonstrated potential purchasing power and authority to enter contracts

Using AI in lead management results in 50% more appointments than other methods. Those appointment are shorter and more productive, too, since businesses can target customers who are ready to buy.

The overall reduction in call time averages around 60% without damaging customer satisfaction rates. That’s why 79% of the top sales teams use data science to power their lead management.

Intelligent customer profiling

Knowing who the customer is and what they want is key to both marketing and customer service. Data science removes the potential for human biases about customers. Specifically, it looks for what customers have in common and groups them by that instead of imposing arbitrary demographic boundaries.

Profiling software analyzes all available data on a company and its customers to find previously unnoticed similarities. These hidden connections can be then used to drive revenue in different ways.

They’re particularly good at identifying customers with the highest potential lifetime value or highlighting potential extra services current customers might enjoy.

A great intelligent customer profiling success story in this arena comes from video distributor Giant Media. After using data science to build data-driven customer profiles they found 10,000 new leads across the United States.

500 brands were in their desired New York City market. The software even isolated 118 businesses that matched Giant Media’s idea profile and provided contact information fast enough to enable effective sales calls.

Improving customer experience

One theme keeps popping up in sales and marketing discussions: customer experience is king. It’s predicted to be the primary brand differentiator by 2020. 86% of customers value a good buying experience over cost and will pay more for better service. Once they’ve had that positive experience they’re 15 times more likely to purchase from the same vendor again.

Source: Concepta, Inc

What is considered “good” customer service? Besides obvious factors like reliable customer service and solid quality, personalized service seems to be the key to winning over customers.

Data science provides insights that allow for that personalized service on a large scale. It can offer tailored interactions such as:

  • Suggesting products based on past purchases
  • Retargeting customers at appropriate intervals (for instance, reorder reminders for pet food or garage coupons as a customer’s vehicle hits certain milestone)
  • Reminders around holidays (like Mother’s Day or family birthdays)

In short, creating an outstanding customer experience requires knowing what the customer values and being able to offer it on demand. Data science is invaluable here. Chatbots in particular are useful for providing assistance that customers need, when they need it, and in an accessible format.

Timely sales forecasting

Sales forecasting without modern data science methods takes far too much time. Reports are huge, hard to get through, and don’t arrive in time to help sales staff. As a practical compromise sales staff often rely on wider-scope numbers which are more readily available instead of targeted data on local customers.

Data science – specifically predictive analytics – can provide near-real time information on what’s selling, where it’s selling, and who’s buying it. This prepares companies on a structure level to spot opportunities and make the most of them.

It increases overall enterprise flexibility. Plus, sales staff can use the information to build better pitches, improve relationships with their customers, and generally make better use of their time.

Supply chain management

Managing the supply chain feeds directly into revenue. After all, companies can’t sell what they don’t have. Data science provides insights that enable more efficient internal operations, which leads to better margins. To get specific, insights gained from data science can be used to:

  • Keep enough inventory on hand to meet demand, regardless of season
  • Make deliveries on time despite potential delays
  • Schedule services more accurately so customers can plan their day

Pitt Ohio Freight Company saw a major boost in sales after applying data science to their supply chain problems. They trained algorithms to consider factors like freight weight, driving distance, and historical traffic to estimate the time a driver will arrive at their delivery destination with a 99 percent accuracy rate.

Their customers were highly impressed. Pitt Ohio now enjoys $50,000 more in repeat orders annually, and they’ve reduced the risk of lost customers as well.

Price optimization

Pricing is tricky. The goal is to find a profitable price that the customer is happy to pay so as to ensure repeat business. An enormous number of factors affect pricing, and it’s hard for humans to tell what’s important and what isn’t.

Data science has no such handicap. It can be applied to customer, sales, inventory, and other market data to uncover what actually influences a customer’s willingness to buy at a specific price. Based on that, companies can find the ideal price to make everyone feel satisfied with the purchase.

Airbnb uses a dynamic system based on this concept. The company tracks local events, hotel trends, and other factors to suggest the best price to its hosts.

This is a major part of their business strategy since hosts aren’t usually professional hoteliers; that guidance is necessary to keep hosts happy and listing with Airbnb.

Some hotels have a more complex system for setting prices. Rates used to be uniform across the board. Changes were only triggered by season or maybe rewards club status. Data science opened the doors to a more egalitarian pricing strategy.

Now each user can be shown a customized price based on a number of objective factors.

  • Is the trip for business or pleasure?
  • What rates did the customer receive for past stays?
  • How valuable is the customer as a client?
  • Does the customer have a booking at a competitor which they might be willing to change?
  • Will the customer be using cash or points?
  • Does the customer have past incidents of bad behavior at the family of hotels?

Interestingly, customers who caused expensive trouble during previous stays may be shown higher rates to discourage a booking.

Marriott, who were early adopters of data science in the hospitality industry, is an interesting case. The hotel chain was generating $150-200 million per year in the 1990s by intelligently managing their Revenue Per Available Room, or RevPAR. It’s still growing at a rate of 3% a year.

As a general trend, applying data science to price optimization increases revenue by 5-10%. The most benefit is seen in season-dependent industries such as hospitality.

Looking to the future

Industry leaders are taking note of these benefits. As a result, data science is fast becoming the preferred way to fuel digital transformation efforts. Global revenues for big data and business analytics were up 12.4 percent last year, and commercial purchases of hardware, software and services to support data science and analytics exceeded $210 billion.

Source: Concepta, Inc

Companies who hesitate to adopt data science will soon be left in the dust by their better-prepared competitors. Now is the time to make the business case for integrating data science- before it’s too late.

Not sure where to start? Concepta can advise on and customize powerful data science systems to meet your specific needs. Schedule a free, no hassle consultation to find out how!

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Automatic​ ​Customer​ ​Classification:​ ​The​ ​First​ ​Step​ ​to Segmentation​

automatic customer classification

Customer segmentation is a critical part of identifying your best customers, but you can’t do it until you know more about them.

That’s where automatic customer classification comes into play.

This article will explain the distinction between classification and segmentation, outline the core concepts of classification, and highlight the actual business benefits of automatic customer classification.

Classification Vs. Segmentation

In simple terms, segmentation is applied to the results of classification.

Segmentation can’t happen without having some characteristics to use, and classification is pointless if the information is not put to use.

Customer classification is the act of seeking out and identifying common traits in a group of customers.

It answers a broad question: what is similar about these people and their purchasing habits?

Segmentation takes that a step further by subdividing customers according to those similarities.

It answers a more focused questions: what is the most useful way to group these people based on the commonalities found during classification?

Unbiased results

Automatic classification involves using an algorithm to sort customers as data about them becomes available.

When done right, it incorporates all data resources regardless of whether a person thinks the information may be relevant.

Customer data is thus drawn from the silos where it tends to collect and put to work.

There are undeniable benefits to using automatic classification methods.

When a person does classification – even when setting up a series of filters – they can only filter by what they think might be relevant.

People typically end up using demographics as differentiating factors (age, family structure, income, residence).

Using diagnostic analytics, an algorithm can find unexpected points of similarity that better predicts customer behavior and potential lifetime value.

Algorithms have no preset bias about how people of various socioeconomic brackets or regions behave.

The labels they generate are based solely on patterns found within the given dataset.

They might reveal unexpected commonalities in high-value customers such as path to purchase, lifestyle factors, similarities gleaned from touchpoint analysis (how recently the customer interacted with the brand before purchase), or other factors that are hard for human analysts to detect.

Cluster analysis

A common method of customer classification is cluster analysis, also known as cluster modeling or cluster-weighted modeling.

Cluster analysis gathers data points into clusters based on both their similarity to each other and their difference from other clusters.

Some of the more popular clustering algorithms are:

  • K-Means clustering: Clusters data points together based on Euclidean distance. The amount of clusters is determined organically in response to the data.
  • Hierarchical clustering: Creates a ranked group of clusters. It either begins with all data points in their own clusters and moves up to pull together similar points or starts with all data in one cluster and moves down, breaking out data points that are no longer similar to each other until each is in its own cluster.
  • DBSCAN: This is a very common clustering algorithm based on distinguishing coherent clusters from outliers. It also doesn’t need to be fed the number of clusters before sorting.

Most of the time, several techniques will be combined to realistically represent the data.

Benefits of automatic customer classification

Automatic classification can handle more data than human analyst.

It’s faster and more accurate, making the best possible use of a company’s customer data.

Letting machine learning determine what characteristics actually impact value uncovers useful information about the customer base.

These insights suggest ideas for future products and services or areas where the company can improve to widen their appeal.

Finally, automatic customer classification informs a highly precise customer profile.

Better profiles lead to a more personalized buying experience, where customers are treated as individuals with different needs instead of being presented with generic offerings.

Looking forward

With buying experience fast becoming the leading differentiating factor among brands, understanding who customers are is of paramount importance.

Automatic customer classification is the first step on the path to a closer, more profitable connection with customers.

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Intelligent Customer Profiling for the Sales Team

intelligent customer profiling sales

Much of the press surrounding artificial intelligence has focused on its uses in areas like customer service or marketing or inventory management.

Sales is often overlooked or lumped in with marketing, but artificial intelligence- more specifically intelligent customer profiling- has the potential to revolutionize the way the sales team operates on a user level.

More data, less guessing

Intelligent profiles take the guesswork out of creating buyer personas.

Instead of relying on instinct, individual experience, or preconceptions about a product’s primary audience, sales staff can access the collected experience of the company to build an accurate picture of their customers.

Data-driven lead management

With intelligent customer profiles, sales teams have a good idea of who will be interested in their product and who isn’t yet at the buying stage.

As a result leads increase in both number and value.

Companies who use artificial intelligence in sales report a 50% increase in leads and appointments.

That’s why 79% of high-performing teams incorporate predictive intelligence (including intelligent profiles) into their lead management procedures.

Labor optimization

Better lead management has a secondary benefit.

It helps managers make the best possible use of their sales staff.

Rather than devoting hours to persuading customers who aren’t ready to buy, agents are assigned to those most likely to make a purchase.

They spend less time pitching and more time signing deals.

Teams that prioritize their leads with predictive analytics like this see call time reductions of as much as 60-70%.

Lower overhead

Integrating AI into the sales process lowers labor and overtime costs, but that’s not where the savings end.

A Harvard Business Review study found overall cost reductions of 40-60% after sales teams adopted artificial intelligence to evaluate customers.

The bulk of this comes from trimming unnecessary travel and meeting expenses.

Because managers can predict the potential value of a client, resources aren’t wasted where there’s little chance of success.

Focus on relationships

Time and again customer experience has been proven to be a leading competitive factor in business.

86% of customers prioritize a positive buying experience over cost.

By 2020, customer experience is predicted to overtake price and product as the key brand differentiator.

With intelligent profiles, sales agents arrive at meetings armed with the information a particular customer is most likely to want or need.

They know in advance where the best opportunities for upsells lie, and they have an edge in scheduling future orders because they know how similar customers have behaved in the past.

Preparation like this makes customers feel personally valued. That leads to better customer relationships and higher retention rates.

Practical tools for everyday use

Intelligent customer profiles aren’t only useful for strategy.

They provide practical sales guidance on an operational level.

Sales teams can improve their performance significantly by using this technology to better connect with and serve clients.

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Find Your Best Customers Through Intelligent Customer Profiling

intelligent customer profiling

In the past, digital marketing used the same techniques as print advertising.

Marketers made their best guess about the demographics and keywords relevant to their product, measured a campaign’s success, and refined their approach from there.

Machine learning technology has changed that.

Now companies can use predictive analytics to build intelligent customer profiles, allowing them to maximize revenue by knowing which customers to target and how.

Underused Resources

The biggest problem from a digital marketing standpoint isn’t acquiring useful data.

Companies make data every day.

Sales patterns, loyalty programs, website traffic, customer volume, social media activity- all of these result in data that could provide valuable insights.

The real problem is that companies have struggled to find a way to effectively interpret their raw data into marketing direction.

More data has been generated in the last five years than in all of human history before then, yet only 0.5% is ever analyzed and put to work.

Instead, data ends up siloed within different departments of an organization.

Parts of it are used, but the bulk of a company’s data ends up aging out of usefulness in databases.

Deeper Customer Understanding

Predictive analytics can take advantage of that existing store of information by using it to build models of customers based on data, a process called intelligent customer profiling.

Although customer profiles have been a popular marketing tool for years, until now their creation was subjective.

There was no reliable way to know who was buying a product and why, so profiles tended to be limited to demographics and whatever information could be gleaned from focus groups or customer interviews.

Intelligent profiling- sometimes referred to as predictive customer intelligence– is worlds beyond that approach.

It draws on data gathered through the course of normal operations to add refined demographic and psychographic details to the customer profile.

Targeted Marketing

Having a better grasp on customers has major benefits for a company.

For starters, it provides a reality check as to who uses their services the most and why.

Is it the ideal customer from the company’s buyer persona, or is there growing popularity among a completely different demographic?

Customer profiling also serves to connect the data-gathering back end of a business with the action-focused front end.

In other words, predictive analytics is where the business value of data science lies.

It provides concrete suggestions that can steer the marketing team in the most profitable direction.

These are just a few questions intelligent profiling can answer:

  • Who is most likely to be a repeat customer?
  • What types of customers offer the highest ROI?
  • Who will respond to an email?
  • Why do consumers choose this company over its competitors?
  • What indicators suggest a customer will qualify for a program or loan?
  • Is marketing reaching the right sort of customers?

These profiles are powerful tools for the marketing department.

They can help reach full potential revenue on existing customers by revealing what matters most.

Alternately, they can reveal opportunities to target prospective customers, ones that weren’t included by the subjective buyer personas.

Intelligent profiling benefits more than the marketing department.

It guides the sales team in creating the perfect pitch for every profile type, each informed by hard data that reveals what matters most to that customer.

If you’d like to know more about how predictive analytics can empower your sales team, watch this blog.

We’ll be posting the second part of our customer profiling series next month.

Parting Thoughts

Knowledge is power, but only when it’s used.

Intelligent customer profiling is the path from inert data to actionable insights.

Having trouble managing the output from a handful of marketing programs? Concepta can unify your analytics through a single custom dashboard to display all your data in one location.

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