Subscribe to the Teradata Blog

Get the latest industry news, technology trends, and data science insights each week.

I consent that Teradata Corporation, as provider of this website, may occasionally send me Teradata Marketing Communications emails with information regarding products, data analytics, and event and webinar invitations. I understand that I may unsubscribe at any time by following the unsubscribe link at the bottom of any email I receive.

Your privacy is important. Your personal information will be collected, stored, and processed in accordance with the Teradata Global Privacy Policy.

Customer "Jobs to be Done"

Customer "Jobs to be Done"
Customer ‘jobs to be done’ is a concept developed by professor Clayton Christensen at Harvard Business School. The idea is that customers do not ‘buy’ products and services, but ‘hire’ them to get a job done. To give a good example of what this concept is, in his presentations, professor Christensen has used the idea of people ‘hiring’ food in the morning to stave off hunger on the drive to work.

The above example he gives is a real-life use case of the theory, where the company he worked for at the time was helping a fast food chain understand how to boost sales of their milkshakes, by understanding why people ‘hired’ their milk shakes and why their sales were significantly higher during the morning commute.

It turned out that people ‘hired’ the milk shakes on their drive to work as they could be easily consumed while in the car. The beverage didn’t leave a mess all over the car (like some breakfast foods can) and they staved off hunger better than other solid food items. Ultimately, the milkshake was the best product to get the job done.
This concept is not just for food, it can be applied to every industry. For example: auto workers hiring robots to assemble cars, shipping vessels hiring containers to transport cargo and retailers hiring shelfs to display products.

A job is a problem a person is trying to solve. A ‘jobs to be done’ perspective focuses on what causes a customer to buy a product rather than relying on the attributes that are merely correlated with buying behaviour. Jobs are not adjectives or adverbs. A job to be done typically starts with the words: “Help me do...”, “Help me avoid...”, or “I need to...”

Opportunities for innovation

While all this seems obvious, very few companies use the perspective of ‘getting the job done’ to discover opportunities for innovation. By deconstructing a job from beginning to end, a company gains a complete view of all the points at which a customer might desire more help from a product or service, at each step of the job. With a job map in hand, a company can analyse the biggest drawbacks of the products and services customers currently use.
Job mapping differs from process mapping in that the goal is to identify what customers are trying to get done at every step, not what they are doing currently. By mapping out every step of the job and locating opportunities for innovative solutions, companies can discover new ways to differentiate their offerings.
While paradigm shifting visions are unlikely to be found under a pile of data, finding undiscovered jobs is by all means possible through data analytics. A key prerequisite is to attain each data point (e.g. process, customer interaction etc.) at a very granular level. By stripping the data from preconceived notions of how a product is “supposed” to be used, one can then let the data speak for itself – e.g. finding new uses of a product, differing from what was initially intended. A criterion, of course, being that a few of the company’s own customers are treating new ‘jobs’ with the product. This requires advanced analytics and handling vast amounts of data in equal measure – something we at Teradata specialise in.
An area of machine learning which deals with finding patterns when you don’t know what you’re looking for is called unsupervised learning. The idea is that rather than trying to train a model to identify an already labelled event, image or property (e.g. something we know, like an image of a cat, or a customer churning) one can let algorithms extract paths, patterns and connections that were previously unknown. When applying unsupervised techniques, it becomes increasingly important not to fall into the toxic, yet common, trap of designing or tuning the algorithms in such a way that believed truths are confirmed.
Many companies are focused on the product or service they’re already developing, or what the competition is offering, rather than on the help the customer needs to execute the steps in a ‘job’. When the job is the focal point of value creation, companies can not only improve their existing offerings but also target a new, uncontested market space, aka. ‘blue ocean’. Maybe they can even take a leap by disrupting themselves.
An excellent example is when music player manufacturers were concentrating on helping customers listen to music, Spotify reconsidered the entire job of music management. They knew It wasn’t about the device, it was about the job to acquire, listen, organise and share music, and how to design a business model to fit those jobs.

Not until companies understand what their customers are ‘hiring’ them for, can they find and decide on which enablers are needed to do the work of fulfilling the promise; such as enterprise components: capability maps, processes, activities, data and technology, to support those vows. Companies should, on a regular basis, step back and question if their products & services offered are still relevant, or if there are problems their customers have but are left on their own to solve.

The Swedish car company, Volvo, is on the mission of helping people make better use of their time and travel more efficiently. Their autonomous driving concept car, 360c, is a perfect example of how a company can elaborate on what ‘jobs to be done’ they are in the business to solve for their customers. The car has a range of potential uses: a sleeping environment, mobile office, living room and entertainment space to name a few.
For the customer job of sleeping, Volvo could rival short-haul air, bus and train transport options, as well as highway motels. Volvo says: “The 360c sleeping environment enables first class private cabin travel from door to door, without the inconvenience of airport security, queuing, noisy and cramped airliners.” We, for sure, won’t be surprised if in the future AirBnB’s competitor will be a car manufacturer. 

Putting it into practice

Companies integrated around a ‘job’ can achieve market differentiation and avoid disruption. Jobs to be done generally have two dimensions. The practical role the product or service fulfils and emotional – the feeling one gets from owning or using the product or service.

So how could you go about discovering jobs to be done? Here is an example of a three-step approach
  1. Understand why customers choose you other than for the job itself
  2. Reflect deeply on personal experiences of your customers
  3. Observe current customers and identify the workarounds or compensating behaviours customers use

Once you’ve figured out each step and captured the data, preferably on a very granular level (and in real-time if appropriate), you can start to chisel out customers’ true needs and what they want to hire your company’s products and services for.

Next up is to develop a method for picking out the right data points to help fulfil or meet those needs. Business development is no longer about coming up with a product or service that fits all - it’s about personalising data, acknowledging that each customer may have its own specific ‘job’.

This is where analytical techniques play in, to decode the bottomless pit of possibilities and turn big data into small decisions. However, just as finding what jobs your customers want to hire your personalised data story for, remember the core essence for which they will continue doing business with you: customers want you to know them, look after them and reward them.

Teradata is uniquely positioned to help businesses understand their data at a granular level through our consulting services and advanced analytical platform, Teradata Vantage, which allows customers to analyse anything, anywhere at any time.

For more information on how Teradata has helped companies understand their data and customers’ needs to get actionable results, visit our customer page for testimonial videos and case studies:
Portrait of Anette Bergendorff

Anette Bergendorff

Anette Bergendorff is a Sr. Industry Consultant at Teradata. She supports and inspires businesses to understand what they might not be doing with data today but will be wanting to do tomorrow. She believes that companies who manage data in an ethical and transparent way will be able to exceed customer expectations and grow revenue. Anette has 25 years of experience from the Insurance and Financial Services Industry working in areas of business architecture & development, information & data strategies, organisational design, business operations and change & transformation programs. View all posts by Anette Bergendorff
Portrait of Henrik Atteryd

Henrik Atteryd

Henrik Atteryd is a Sr. Data Scientist at Teradata Consulting. He works with customers from varying industries to realise commercial value from leveraging advanced analytics on big data. Henrik has a background in Statistics and 5+ years of experience from hands-on as well as leading roles within the field of analytics, data science and Business Intelligence.
  View all posts by Henrik Atteryd

Turn your complex data and analytics into answers with Teradata Vantage.

Contact us