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.
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).
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.
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.
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.
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!