How to price analytics applications

How to price analytics applications

There are plenty of alternatives in pricing any analytical application. The best outcome delivers commercial returns and is easy for your salesforce to sell.

There are many ways that you can price an analytics application, but the key pricing consideration is always finding the approach that makes the most sense for your business. You want to price your product in a way that is easy for customers to see the value and for your salespeople to sell.

There are generally six pricing options available:

  1. Modular pricing – where you charge an extra amount for the analytics app
  2. Base pricing – the analytics app is bundled into your product and the base fee is just increased
  3. Free – bundle the analytics app but just absorb the cost
  4. Functional split – charge multiple fees for each component of the analytics app
  5. Data split – charge customers only for the type of data they access, with higher charges for executive level data and lower or operational data
  6. Content split – charge customers for the content they use, with higher charges for executive dashboards and lower for operational dashboards

 

There are advantages and disadvantages to each of these pricing methods, but the end goal is the same – you want to achieve the best commercial outcome for your business.

Simple pricing makes it easy to buy, and sell your application

The most important thing to remember when pricing an analytics application is that it should be easy to buy. If your sales people are confused about how it’s priced, imagine what it’s like for your potential customers.

If something is easy to sell it’s generally also easier for your customers to buy. The key to making something easy to sell is for the value proposition to be clear. That’s why I believe it’s generally important to separate your analytics app from your core product when determining how to price it. This requires you to look at the analytics app independently and give it a distinct value. This value should be something that you can articulate internally and also help your customers understand.

If you can articulate the value of the standalone analytics application internally, you can also justify why you’re spending money and time on this feature. By taking a modular approach, your business can clearly identify the revenue that is attached to the analytics and its ROI. This may make it easier when it comes to deciding how to allocate resources in the future. Similarly, if your customers can understand what information they will get and how it will help them make better decisions, they will be able to articulate the ROI of your embedded analytics to their internal stakeholders and be more willing to purchase it.

For more on how to build great embedded analytics applications, download our new 70-page ebook: A Guide To Embedding Analytics Into Your Application

Keep your pricing model consistent

Your existing pricing model is the best place to start. If your model is based on number of users, then this should form the basis of the new pricing model. Introducing a whole new model will only confuse your customers and sales team. Remember, you’re trying to add value to your existing customers, not create a new point of negotiation or make them wonder whether they should look at a competitor’s product.

This is where modular pricing has an advantage. It creates a separate price point for your analytics solution – i.e. your core product is worth X and the analytics solution is worth an additional Y. It’s then clear to customers what they will pay for the additional value.

Within modular pricing, you can also add different levels of pricing by splitting the product by function, data or content. This means a functional user, someone who creates content, might be charged a higher price than someone who just consumes dashboards and reports. Similarly, a data or content split would sell your data or content in different blocks – executive data that’s worth a lot more to the C-Suite might be priced at a higher price point than operational data that is perceived to be of lower value.

While the most consistent pricing approach may be to just absorb the analytics into your core product, this isn’t necessarily the best commercial option. It may be easier to sell, and your customers will get more value from the core product, but it doesn’t recognize the value that the analytics give your customers. Essentially, you could be leaving money on the table by not valuing the analytics component on a separate basis.

In addition, absorbing analytics into the pricing of the broader product can cause internal problems. From a P&L perspective, analytics becomes just another cost centre without any clear value attached to it. This may make it difficult to sell internally down the track as you need to invest more time and resources.

Regardless of which pricing model you choose to adopt, the aim is always to achieve the best outcome for your business and your customers. The product should be easy for your sales reps to sell, and simple enough for customers to identify the value it provides them. Happy sales people, satisfied customers, and a growing bottom line are the ultimate goal of any pricing strategy.

This article is built on the research and content from our new 70-page ebook.

Download the eBook now: A Guide To Embedding Analytics Into Your Application

Thanks for trying Yellowfin

Please complete the form below to request your copy of Yellowfin today.