Increasing your average deal size is a key lever in boosting your revenue growth. It may seem obvious, but it's easy to forget in the face of growing customer acquisition efforts or revenue challenges.
To maximize revenue growth, companies need to understand how to grow their customers (such as with loyalty programs and upselling) while at the same time minimizing churn. In this guide, you’ll learn how to predict the deal size, so you can prioritize leads that are likely to result in big wins.
With this knowledge, you'll be able to better allocate your resources and spend your time on deals that will give you the best ROI. After all, to get to the next level in your business, you'll need to focus on the next level of deal size. Larger deals will require more resources--data, people, and money. With Akkio, you'll be able to confidently budget for these needs.
Additionally, this will ensure that small deals don't eat up your resources and distract your team from the big wins.
To get started, sign up to Akkio for free. As with any machine learning task, the first step is getting historical data and picking a column to predict.
We’ll use a synthetic dataset of retail sales from Kaggle, titled Dummy Marketing and Sales Data. This dataset includes several thousand rows of various advertising spend values, across TV, social media, radio, and influencer programs, and the resulting total sales deals value.
While this dataset may look different from your internal B2B sales data, the principles are the same: identify what metrics contribute most to sales, and be able to predict the size of the sale on new data, whether it's a new lead or a new marketing plan.
Crucially, the file includes our desired target variable, called sales, or the amount of sales in the millions of dollars. We can use machine learning to predict the amount of sales from new data, and then prioritize efforts accordingly.
There are many factors that could affect sales, including seasonality, product availability, holidays, and more. With Akkio, you can build custom models to address the specific drivers of your business. And with our drag-and-drop interface, you can build any predictive model you want.
First, we’ll just download the dataset as a CSV, and upload it to Akkio in a new model flow. You'll get a preview of the dataset, including modifiable data types, columns, and row count.
Now, we can click on the second step in the AI flow, which is “Predict.” Under “predict fields” you can select the column to predict, named “sales.”
You can add or remove as many columns as you’d like, and we'll build a model based on the selected columns. Then just hit “Create Predictive Model,” and you’re done.
You can select a longer or shorter training time—ranging from 10 seconds to 5 minutes—for potentially more accurate models. Keep in mind that longer training times will not always necessarily perform better.
Also note that you don’t pay for model training time, unlike with many typical automated machine learning tools, so feel free to build as many models as you’d like.
After a model is created, you get a simple overview, which highlights the top fields, model prediction quality, sample predictions, and more.
Armed with these insights, we can now optimize our marketing and sales strategy. For instance, we can see that TV ad spend had an outsized impact on deals value, while radio and influencer campaigns had minimal impact. We can then optimize our TV ad spend with this insight, and use the resulting sales value as an indicator of how effective that spending was.
There's a famous quote by department store mogul John Wanamaker: "Half the money I spend on advertising is wasted; the trouble is I don't know which half." While it's hard to know exactly how much of your marketing budget is being wasted, using Akkio to build predictive models can help you prioritize where to spend your money based on real business insights.
There’s also the option to “See Model Report,” which lets us easily collaborate with others and share these model details with anyone, even if they don’t have an Akkio account. In short, we’ve discovered that it’s very easy to build an accurate model to optimize inventory.
Now that we’ve built a model, it’s time to deploy it in the real world.
With Akkio, it becomes trivial to deploy complex machine learning models. Deployment via web app is an easy way to instantly serve predictions. It’s also possible to deploy through Salesforce, Google Sheets, Snowflake, and the Akkio API, with many more methods coming soon.
Further, you could deploy the model in practically any setting with Zapier, a no-code automation tool that connects tools with “Zaps.” Zapier allows you to link up thousands of different tools, so no matter where your data is coming from, it’s easy to connect it to Akkio to get a prediction, even if you don’t have any technical expertise.
If you’re looking for more technical power, you can also use Akkio’s API, which is formatted as a Curl command, allowing you to send a GET request that contains your flow key, API key, and input data, and get a prediction back.
We’ve explored how deal size can be a key metric for revenue growth, and how to optimize your marketing strategy by analyzing the impact of your various marketing channels. With Akkio, you can build predictive models on your data to better optimize your business.
Traditional methods of marketing can be expensive and ineffective, but with Akkio you can build AI models that predict the value of your customers, and use those insights to optimize your marketing strategy.
To learn more or try out a predictive model for yourself, sign up for a free trial.