There are numerous methods to market data analytics services, but let’s start with the broad strokes of the process. In this article, we will examine what it means to be an agency that sells data analytics services, followed by a more in-depth discussion of the sales process. We will also provide you with advice on how to find clients who require data analytics services!

The process of applying scientific/statistical concepts and procedures to data in order to derive information and knowledge is known as analytics. It allows researchers to take a fresh look at old data, exposing previously unnoticed patterns, trends, or linkages.

Most ‘data-driven’ conclusions are as good as a random guess if scientific or statistical ideas are not employed. This is why having someone who knows these principles at the helm of the data is vital.

However, many analysts spend their time on technical activities such as data cleansing or implementation.

They are preoccupied with activities that are necessary but not always favourable to growth. Furthermore, few agency clients can afford to pay for all of the billable hours required to complete the two projects.

There are typically 4 buyer personas you’ll come across in this journey –

IT – Responsible for Data infrastructure and governance and assessment of Applications.

Governance and Security being top priority

Line of Business – Responsible for driving the Purpose; hence very important to map and empathize with. Heads of Marketing, Finance, Sales, HR, Supply-chain typically fall under this section.

Value (revenue, time-to-market, cost reduction, etc.) is top priority.

Digital Transformation/ COE team – Large enterprises have shared-services teams to own and deploy analytics. These teams are called Digital Transformation team, Innovation teams, Center of Excellence, etc. This department usually owns the analytics projects in delivering the value to LOB. Sometimes, the Chief Data Officer or the Analytics Head falls under this persona type.

Value, creating a data appreciation culture and Collaboration is top priority

Procurement – Understanding the sales process and mapping the right procurement folks helps build a proposal that is sale-able

While these are no different to those involved in selling any enterprise class application, here, they have specific responsibilities and priorities as mentioned above.

Who (industry verticals) buys analytics?

As a Salesperson, you are expected to know about how the buyer’s industry work and what are the levers that are critical to their growth. While Analytics is leveraged by most industry verticals, some of the leading industries that extensively use analytics are:

1. Retail
2. Manufacturing
3. Health Sciences
4. Ecommerce
5. CPG
6. BFSI
7. Telecom
8. Automobile

In each of these industries, besides the areas mentioned in the respective links, there are also common functions such as Marketing (as we saw in buyer personas) which leverage analytics extensively in multiple aspects.

Read Also: What Are The Critical Success Factors For Big Data Analytics?

Data analytics is a must-have service for each creative agency. Whether your clients need to monitor current campaign progress or justify future purchases, data analytics may be combined with almost any creative product. How do agencies market data analytics now that we’ve established why they should utilize it?

Here are four recommendations for working with businesses on a data analytics retainer. These pointers apply to both agencies and in-house marketing teams.

SELL IN A DISCOVERY FOR THE CURRENT STATE OF DATA AND REPORTING

Gain access to your customers’ data analytics needs by running a discovery. During a discovery, you’ll ask questions around what data your customers have and how they use it. From there, you can develop an understanding of their business requirements, data sources, challenges and pain points so that you can create a solution tailored to them.

PITCH A PROJECT TO CLEAN UP THEIR TAGGING IMPLEMENTATION OF EITHER GOOGLE ANALYTICS OR ADOBE

Even if you already do web analytics for your clients, pitch a project to clean up their tagging implementation of either Google Analytics or Adobe. Client-side often have only one person with experience setting up Google Tag Manager or Adobe Tag Manager accounts, and many times they are still using templates and not customizing it for each client. Clients may not know what they are paying for, which makes it easier to talk about the pain points of data abysse.

PROVIDE REGULAR STATISTICAL ANALYSIS FOR WHAT INCREASES MARKETING PERFORMANCE. NOT JUST A DASHBOARD.

Retainers are an important part of marketing analytics. A lot of agencies provide dashboards, which is only a small part of what clients need to take actionable data and turn it into marketing performance improvements. To be successful in building data-driven marketing retainer relationships, you’ll have to go beyond providing just dashboards and make sure you’re giving your client regular statistical analysis for what increases conversion rates.

INCREASE THE RETAINER BY PITCHING THE DATA-DRIVEN STRATEGIES

Agency growth is dependant on increasing retained relationships. Whether you’re trying to retain a client or snag a new one, the best way to increase a retainer is by pitching data-driven strategies. In short, show them how they’ll get more value from your work than they would from their own resources. Not only does it solve their problem—they can save money by hiring you instead of taking it in-house—but it positions you as an expert worth retaining for future projects.

Besides having the traditional Sales abilities, which we won’t cover here, the Analytics Sales person should be highly outcome-oriented and have the following non-negotiable traits:

• a high degree of curiosity – being inquisitive, investigative, questioning and being a keen listener to learn something new
• high appreciation of Data; what, where and why of Data, how is it collected and who collects it, why is it collected, how is it consumed
• aptitude to be able to empathize with business and jot the dots (problems) and further able to connect (link) them
• should have clarity in fundamentals of Data Science, ability to assess what type of problems can be solved using analytics and how
• understanding of basic statistical methods such as (but not limiting to) Regression, Classification, Shrinkage, Nonlinear models, Decision Trees and Unsupervised learning
• strong ability to zoom-in and zoom-out of scenarios; ability to see the big picture yet knowing how to go to the most granular details. Remember, if the client sees no value (revenue, time-to-market, reduced cost, etc.) the purpose is lost and leads to existential crisis
• know the nuances of line of business. What are the typical challenges, what are the domain specific challenges, what are the developments in that space, who in the client’s competition is adopting newer ways of solving these problems, etc.
• demonstrate strong collaborative skills with client stakeholders and internal stakeholders (presales, delivery and customer success teams)

This sums up why, what, who and how to sell analytics.