Wednesday, July 17, 2024

The Benefits of Pricing Your Product for Value

By David Ronald

Are concerns about pricing keeping you awake at night?

If so, it’s understandable—price your product too low and you leave money on the table; price it too high and you can say goodbye to sales that could have made your year.

SaaS companies have relied on subscription-based pricing for more than a decade. Subscriptions are a popular way to sell SaaS solutions because transactions are relatively uncomplicated. Customers can budget for the purchase, and SaaS providers can forecast revenue with a high degree of precision.

However, today’s customers are tired of “shelfware”—they don’t want to waste money on seats that go unused or pay upfront for solutions that may suffer from poor adoption. Usage is variable by nature, challenging to predict, and represents an unknown value-to-cost ratio.

Customers prefer to pay for exactly what they use.

A great product married with an easy-to-understand consumption-based pricing model can pack a powerful one-two punch for your business.

Benefits of Usage-Based Pricing

Companies that use consumption-based pricing are experiencing 38% faster revenue growth over their subscription-based peers based on research from OpenView.

Usage-based pricing not only provides business benefits, but it also helps improve your customers’ experiences: 

  • It better correlates product usage with pricing and eliminates the risk of “black box” pricing associated with other approaches.It shifts cost control to your customers, providing them with maximum flexibility.
  • It provides a low entry price point and allows customers to experience the full product regardless of company size.
  • It improves customer retention since it does not require customer to cancel their plans during periods of lower usage.

Consumption-based pricing compels SaaS providers to better understand customers’ behavior and usage patterns. Through metering, you benefit from a continuous flow of data that illuminates each customer’s behavior in real time.

Transitioning to Usage-Based Pricing

Although there is a huge potential upside when you move to consumption-based pricing it doesn’t mean the shift will be straightforward.

Pricing equates to two things for customers: 

  • Buyers must be confident that your pricing aligns with the perceived value they expect you to deliver.
  • Buyers should believe that the value you will deliver is better than the status quo and what your competitors are delivering.

As you prepare to implement a consumption-based pricing model, focus on these five areas to minimize stress and help ensure success: 

  • Pick a value metric that communicates your solutions’ benefits for customers (more on this below).
  • Transform your sales process and compensation structure by aligning your sales team’s comp plan to the way customers derive value from your solution.
  • Rethink your revenue playbooks to account for the variable nature of consumption and the challenges associated with forecasting revenue.
  • Assist your customers in predicting and optimizing spend through consumption transparency, picking the right payment structure, and manage overage scenarios.
  • Leverage data by unifying it in a central repository so you can deliver in a holistic view that provides insights into consumption and your financials on a daily basis.

Let's look now at the first item on the list above.

Picking a Value Metric

One of the biggest challenges is determining how to price your product. A well-priced solution benefits both SaaS providers and customers. It transforms providers into advocates for value, and value is about more than price—it’s about the success of customers. 

The purpose of a value metric is to communicate your product’s value to customers. It demonstrates an understanding as to why customers pay you for your service. Here are three factors to keep in mind when choosing your value metric: 

  • It should be easy for buyers to understand immediately—your value metrics should not be complicated or incomprehensible.
  • It should align with your solution’s perceived value.
  • It should scale and grow in correlation with your buyer’s use.

What value metric is best for your business?

You can begin by recognizing the challenges and use cases that your buyers are trying to solve. Think about how your solution functions in order to solve a problem, and then consider the outcomes your customers track. Here are some examples:
 

Type of Organization

Value Metric

Internet marketing platform

Number of marketing contacts

Monitoring and analytics tools

Amount of data ingested

Authentication services

Number of external active users

Communication platform

Number of SMS messages

Cloud computing service provider

Amount of data stored

Security network

Pricing per feature

Task automation service

Number of tasks

It is important to ensure that it aligns with your delivery costs and will scale and grow with your customers whatever value metric you select—and the only way to uncover this ideal provider-and-customer alignment is through testing. 

This means that you should put your value metric out in the wild and see how it performs with real customers.   

Keep in mind that you’ll need to be patient—a data-driven approach requires implementing, testing, and iterating different ideas. 

It takes time and honing to ensure your value metric aligns with your underlying technology and resonates with your customers.

 

The Possibilities are Priceless

No one will tell you that adopting or shifting to a consumption-based pricing model is easy. However, it’s the right move if it aligns with your company’s overall product strategy and delivers stronger value to customers.

Startups may find it less complicated to adopt a consumption model because everything is greenfield. A try-before-you-buy approach may make it easier to attract customers, but don’t forget that you will chase revenue for a period of time until usage starts to grow consistently. 

Expectations must be set for a longer ramp-up time, and you must have enough funding and investor buy-in to support this strategy.

For mature companies, the process requires plans for handling current accounts and bookings and understanding what a hybrid model looks like. The benefits are that you have time to hone your consumption model to suit your business and can transition sales and finance teams to this new way of operating over time.

Remember that everything starts and ends with data. All data must be accessible from a centralized location and shared securely without friction or integration headaches. Today, that means you must build solutions on a cloud data platform if you want to support growth and enable pricing models that protect margins and drive new revenue.

The end result is delivering more value to your current customers, attracting new customers, and opening up new revenue opportunities—all of which are truly worth the effort.

Thanks for reading.

Let us know what you think of this blog post? Did we omit anything?

And keep an eye open for future blog posts on pricing for your business.

Although I'd like to take full credit for all the ideas presented in this blog post, it's the culmination of ideas from a variety of people and sources—the most significant of these is a paper by the smart people at Snowflake called “Consumption-Based Pricing Playbook", 2022 – it’s a terrific white paper and well worth a read!

Wednesday, July 10, 2024

3 Ingredients of a Successful Content Marketing Strategy

By David Ronald

Content marketing increases demand for what you are selling and should be a key component of your business strategy.

What is content marketing? Content marketing alters the way you sell—it shifts the focus from hyping your products to adding value to prospects’ decision making. Content marketing is about creating relevant, informative and unbiased content that attracts buyers and converts them to loyal customers.

Do you want to make content marketing work for you? If so, here are the three ingredients of a successful content marketing strategy.

1. Help, Don’t Sell

It’s a harsh truth that nobody is interested in you and your business—they are interested in themselves and their own problems. The starting point for your content, therefore, should be “how can we help our customers?” not “how can we sell to our customers?”

Share your expertise freely and be generous with what you know. A good starting point for creating helpful content is to begin with the questions your customers ask you. Answer those questions with your content. Blog about it, make videos about it. The format isn’t the most important thing; it’s the intention that matters more. Use what you know to create exactly the kind of content you know your customers crave.

You can’t separate content marketing from social media, they’re inextricably linked. Social media is one way you share your content – your blogs, your guides, your videos, but your social media updates are content in their own right too. Make them helpful, human and tone done the hype. Share other people’s content, if you know it will help your customers. Share it even if you think it’s too good, and you wish you’d created it yourself. Share it even if it’s so fantastic it hurts.

The biggest thing content marketing can do for you is to build trust in you and your business. Having your customers’ best interests at heart at every stage of the content process—from the subject you choose to write about, to the way you behave online—is the way to build trust, so keep this secret mantra in mind.

(Click here to read our white paper on content marketing: http://bit.ly/1GHDSxB.)

2. Know your content sweet spot

In my experience the number one place where people go wrong with content marketing is by failing to map their content sweet spot—which lies at the intersection between content which helps your customers and the content which will help you grow your business. 

There will be an infinite number of things your customers are looking for online. On the one hand you could share videos of cats doing funny stuff and gain hundreds of social media followers, but it won’t win you any business. 

On the other hand you could talk exclusively about your business and its sales messages and probably nobody will listen. The content sweet spot is somewhere in the middle. Not cats, not self-interested sales promotion. 

The best type of content marketing increases demand for what you are selling. So, focus on talking about applications of your product, instead of focusing on how it works. 

Wistia, a video hosting services company, is a good example of a B2B vendor doing great content marketing—the company has created a series of educational videos that teach viewers how to be better video marketers—each short lesson is a microcosm of some concept within video storytelling, including bulleted lists for easier retention of the subject matter.

By producing videos like these, Wistia has shifted from pitching its products to delivering content that makes its prospects more informed before they buy. And like all good content marketing, these videos are helping Wistia to increase its addressable market—someone not necessarily thinking about creating corporate videos may be excited by this content and embark on a journey that ends up with her signing up for a subscription.

3. Good content marketing is good business

People rarely write letters of complaint these days. 

Instead, they leap onto X (formerly Twitter) and expect you to sort it out. So content marketing isn’t something you can leave to the marketing organization, the whole team has to share the mission. These days, it’s as much a part of customer service as it is a front end marketing issue. 

You need to be a good business—one that acts in the best interests of its customers—through and through. If you’re doing content marketing well, you’ll be creating content and sharing it on social media platforms. 

Being a successful content marketer doesn’t mean creating the shiniest , glossiest, most amazing content and pouring it into the world and waiting for the results. It means using that content to help customers, and start the conversations that develop into long-term relationships. 

A big part of content marketing success is down to what you do with the content once you’ve created it—how you build content creation, distribution, and relationship building into your business model.

Thanks for reading. 

Let us a comment if you found this information helpful.

Wednesday, July 3, 2024

5 Ways Artificial Intelligence and Machine Learning Will Improve Marketing ROI

By David Ronald

Marketing is going through a profound transformation.

A recent BCG report indicates that 70% of CMOs have already integrated generative AI into their practices, with an additional 19% currently in the testing phase. 

Artificial intelligence and machine learning are already having a transformational impact, and this will only increase in the coming months and years.

This is both exciting and unsettling!

The Benefits Are Almost Limitless

When it comes to better marketing campaigns, increased productivity, and accelerated growth, the potential benefits of leveraging artificial intelligence (AI) and machine learning (ML) are seemingly limitless—and valuable use cases for every stage of the marketing cycle are already available today.


Here are five use cases for AI and ML that can have a positive impact on marketing:

  1. Segmentation—Identifying and grouping audiences to tailor campaigns and power faster and attain actionable audience insights.
  2. Personalization—Elevating marketing and advertising impact with better Customer 360 perspectives.
  3. Lead Scoring—Ranking prospects by value and sales-readiness, and power automation to streamline lead scoring and improve productivity.
  4. Forecasting—Predicting the sales pipeline with greater accuracy and optimizing current pipeline in near-real time.
  5. Attribution—Measuring the impact of different campaign touchpoints more accurately.

I'll look at each of these in more depth:

1. Segmentation

Segmentation is crucial for helping marketers identify and categorize groups of accounts and people within a broader target audience and then tailor campaigns to each segment’s interests and behaviors. 

Segmentation is foundational to personalization (which is the topic of the next section) since it provides modern marketing departments with an accurate picture of their customer base and equips them to deliver tailor-made messages to various audiences.

Here are some of the advantages that can be derived from using AI/ML for segmentation:

  • Speed—Segmentation is exponentially faster when implemented with AI/ML than manual segmentation, because an AI can peer through virtually limitless data points nearly instantly.
  • Precision—Marketing teams can analyze a much higher volume of data to segment more precisely segmentation with AI/ML and deep analytics.
  • Increased revenue—An AI can boost an organization’s profitability by enabling marketers to identify and target their most valuable customers with the most pertinent marketing messages.

Segmentation can also increase customer loyalty and satisfaction by surfacing segment-specific opportunities or risks that can be addressed more granularly. 

AI/ML segmentation can, therefore, help organizations optimize profitably by not only precisely identify the segments that are at risk of churn, but also automate corrective measures.

2. Personalization

Marketers need to deliver personalized campaigns aligned to an increasingly diverse customer base. To do this requires understanding their customers holistically.

One of the proven ways to do this is by achieving a Customer 360 perspective. This requires aggregating and unifying customer interaction touch points from the full breadth of their enterprise data—encompassing online transactions, social media posts, forum comments, in-store engagements, third-party interactions, customer support and more. 

Marketers now have exciting new ways to personalize customer touch points and create new, hyper-personalized experiences, including the ability to do the following:

  • Generate large volumes of creative content, including images, videos and innovative, immersive 3D experiences, for brand advertising and other marketing channel content.
  • Micro-target campaign tactics based on individual customer behavior as well as persona and purchase history. 
  • Personalize outreach via chatbots, virtual assistants, dynamic content and personalized ads and recommendations.
  • Identify “next best action” sales opportunities that are tailored to a particular customer’s needs, at a particular moment in time and on a particular channel.

Marketers to run personalized campaigns more precisely than ever before by applying AI/ML to their data.

3. Lead Scoring

With data from multiple sources, marketers can help build a more objective view of each lead, uncover fresh customer insights and gain a better overall understanding of targets. 

Lead scoring can, however, be time consuming and prone to error, since many methodologies still require manual inputs. 

Scores can be inaccurate due to several issues, all of which revolve around data—ingesting large quantities of data from multiple sources can be particularly challenging and data from different sources often lives in different applications and data repositories, so it isn’t readily available for analysis.

Given these challenges and the ever-growing volume of data involved, ML-powered automation has become critical for lead scoring. With machine learning, marketers can deliver an automated system that learns over time and updates automatically using a constant input of data from multiple sources.

4. Forecasting

Company growth is tied directly to an organization’s ability to maximize its sales pipeline with efficiency. But how do organizations see what’s around the corner and plan resourcing needs to be ready to capitalize on pipeline opportunities with speed and accuracy?

The answer is robust and precise sales pipeline forecasting, and AI/ML makes accurate and objective pipeline forecasting a reality. 

An end-to-end machine learning model can be fully customized to incorporate all relevant organizational and customer data, produce near real-time pipeline predictions, and be accessible to all business teams for better organizational alignment. 

5. Attribution

Marketing attribution allows marketers to track which campaigns and touchpoints customers interact with prior to closing an opportunity. 

Without being able to attribute results to campaign tactics, marketers are operating in the dark, spending time and money creating content or running ads or campaigns without understanding which tactics are driving the best results.

That said, accurate marketing attribution is one of the most difficult use cases in marketing, particularly in an era where customers engage with companies through multiple devices, channels and mediums.

Fortunately, data-driven attribution models powered by AI/ML offer increasingly promising options that create flexibility in their many cutting-edge attribution algorithms. 

Unlike common attribution models that are difficult to scale and often produce inaccurate or simplistic results for certain campaigns or channels, data-driven models allow marketers to experiment with a variety of ML algorithms that produce more accurate results about conversion rates at each touchpoint.

There Are Risks

Up to this point I've focused only on the positive aspects of AI/ML. 

No matter how compelling the potential business advantages are, the unprecedented rate of evolution of AI/ML requires that it be handled with great care—marketers must remain vigilant to mitigate the risks of infringing intellectual property, violating data privacy standards and losing sight of foundational security needs.

Buyer trust isn’t just an everyday issue to manage—it’s a foundational pillar of a modern, data-driven marketing strategy, and even a single AI “hallucination” gone uncaught could have crippling reputational and business impact. 

The risks are significant…but so are the rewards.

Thanks for reading.

Although I'd like to take full credit for all the ideas presented in this blog post, it's the culmination of ideas from a variety of people and sources—the most significant of these is a paper by the smart people at Snowflake called "5 Ways AI and Machine Learning Accelerate B2B Marketing ROI". It's well worth a read.