Wondering how to plan your inventory? Or how to predict demand for a product? Perhaps you’re looking to increase sales or reduce waste.
There are many decisions you’ll need to take while running and growing a business. Understanding your sales is the key to making better decisions!
In this post, we will explain what sales prediction is, why you should use it, and how to implement it using machine learning. Read till the end so you don’t miss out on the best way to implement sales prediction in your business.
Sales prediction is the process of understanding what consumers will actually buy, how much, and when. At its core, sales prediction can be used by businesses to increase revenue and reduce waste.
For example, you could use sales prediction to forecast demand for an upcoming season and place your orders accordingly. This would allow you to avoid over-stocking or under-stocking your product lines. You also wouldn’t have to worry about excess inventory driving down prices, or a product shortage driving consumers towards your competitors.
Companies can base their forecasts on past sales data, industry-wide comparisons, and economic trends. Machine learning is a subset of artificial intelligence that can automate this process. It lets computers make accurate predictions without being explicitly programmed for every possible scenario.
By using historical sales data and other inputs, a machine learning model can create a forecast that is more accurate than a human analyst, and in far less time. This is because it can analyze a huge amount of data at once, from any number of sources.
The most common method for creating a forecast relies heavily on historical sales data. A model is trained on historical customer purchases, using that dataset as input and producing a predicted outcome based on those inputs (in other words, it learns from the past).
Let’s explore the benefits of machine learning across five main areas:
With AI, you can make better decisions across sales team resources, allocating budgets, determining inventory and pricing, and managing contracts and terms. You can also do the forecasting, tracking, and trend analysis that will allow you to make more informed decisions about your sales efforts going forward.
For example, if you're creating a budget for marketing activities, you can use your machine learning model to predict how many additional customers you're likely to get from a particular marketing campaign. The same goes for ordering inventory or managing fulfillment centers: accurately forecasting sales will save you time and money.
You could also use your models to make other types of predictions, like which store displays are the most effective at driving sales, or what pricing strategies work best in different market conditions.
The benefits of sales prediction go beyond just helping you make better decisions. In many cases, it can also help you save money. Predicting demand will allow you to reduce your costs by avoiding over-ordering or under-ordering inventory, and by streamlining supply chain management with more accurate forecasting and reduced wasted resources from excess inventory.
Inventory management is a key area where AI can help sales managers optimize resource usage by streamlining how they source inventory from suppliers.
It offers visibility into inventory levels across geographical regions worldwide, helps identify underutilized assets (or ones that are overstocked), enables efficient re-allocation of excess inventory based on market conditions or forecasts, and identifies opportunities for sourcing better quality inventory at lower costs through new suppliers or channels.
Knowing future sales can help make long-range profit plans, and provision for capital investments like replacement machinery. It can also be used to assess the financial impact of a planned market entry, or to predict if a customer will stay with your company over time.
A key benefit of AI in sales is that it can help you do this more accurately and efficiently. Using a combination of data sources including structured and unstructured information, public records, social media analytics, as well as qualitative techniques like market research and interviews with stakeholders, AI systems are able to generate much more accurate forecasts on their own than humans can (as long as they have the right data).
In this way, sales AI can free up your team to focus on strategic activities, while the AI system takes care of the nitty-gritty of making accurate forecasts, and keeping you informed about the state of your business.
One of the most important tasks for any manager is setting stretch targets for individual team members or teams. However, traditional sales KPIs like revenue don’t provide enough context to make informed decisions about who should be rewarded for hitting targets and who could stand to benefit from additional training or development.
With AI-assisted sales forecasting and goal-setting, managers get more relevant performance feedback that helps them make more informed decisions when setting targets.
For example: does an increase in target volume mean an increase in expected revenue? How does that change if we add another salesperson? How does it change if our customers pass their current inventory onto new customers — will our lead count increase? If so by how much? And would that be sufficient to meet our target volume?
These are all potential questions that managers need answers to before they set new targets at each level along the sales funnel. Data science provides these critical business insights through statistical analysis of large volumes of real-time data as it relates to each stage of the sales cycle.
The more data you have about your customer, the better you can understand them — and the better you can serve them. The challenge is that in a typical B2B sales cycle, there is very little interaction between sales and marketing teams. Data science can bridge that gap by giving marketers insights into their customers’ needs, behaviors, and preferences so that they can develop content and campaigns that are more relevant for those prospects.
For example, if you as a marketer know what features your customers value most when making purchasing decisions (e.g., live chat support), then you will be able to create highly engaging content around those topics on your website and blog. You could also use AI to suggest relevant content based on the search terms your leads are using when entering your website, creating a much more personalized experience for each visitor - all without having to lift a finger.
Demand forecasts can even be used to create highly targeted content, campaigns, and promotions. For example, if you know that a particular product is in high demand this season but your warehouse is low on stock, then you could plan to restock your inventory early so that customers have a more consistent supply of the product. You can then safely market that product, without worrying about stock running out.
In order to create and deliver content that is truly relevant to your audience, you need to understand not only who they are but also what they want. Thankfully, data science can help with that.
Through techniques like predictive modelling and market segmentation, data scientists can identify patterns in customer behavior and use that information to develop marketing strategies that are more likely to resonate with prospects.
For example, consider a case where you want to target high-value customers who have a propensity to churn. Using predictive modelling, you could analyze your customer data to identify which factors are most closely associated with early attrition (e.g., length of time since last purchase, frequency of interactions, etc.). With this information in hand, you could then develop targeted marketing campaigns designed to keep these at-risk customers engaged with your brand.
Machine learning can also be used to monitor your competitors’ activities and predict their next move. For example, if you know that a competitor is planning to launch a new product in the near future, you could use AI to monitor their social media channels for clues about what that product might be. You could then prepare your own launch strategy so that you’re not caught off-guard - or worse, beaten to market.
In addition, by analyzing your competitors’ pricing patterns and sales figures, you can gain insights into their business model and strategic approach. This information can then be used to inform your own business decisions - for example, deciding whether to enter a particular market or adjusting your pricing in response to a competitor’s price cut.
Last but not least, machine learning can help sales teams automate repetitive tasks and speed up processes so that they can focus on higher-value activities.
For example, lead scoring models can be used to automatically prioritize leads so that salespeople know which ones are most likely to convert - meaning they can focus their energy on those prospects rather than wasting time on dead ends.
There are many ways to implement machine learning for sales prediction. Traditionally, technical teams build custom models on top of machine learning frameworks like TensorFlow and Theano.
These teams often struggle with the complexity of setting up and maintaining their infrastructure, which requires an understanding of time series prediction, forecasting methods like linear regression, as well as forecasting accuracy. Further, you’d need technical know-how with tools like Python Pandas dataframes, neural networks like LSTM, alongside data analysis and data mining tools like matplotlib. All this creates a huge barrier to entry for AI.
To address this, people have turned to “off-the-shelf” solutions that provide a ready-made pipeline for implementing machine learning in a scalable and easy-to-deploy way. Some popular examples are Google AutoML and Azure AutoML. While these tools may seem less complex than building your own solution from scratch, they can still be daunting for nontechnical users to deploy and manage internally.
Non-technical teams need a simpler way to do this, which is why we created the Akkio AI platform. With Akkio, you can quickly add powerful predictive models to any business process without having to worry about setting up infrastructure or managing code yourself.
You simply connect a historical data set – whether it’s structured as rows in CSV files or a database in Snowflake. We apply advanced algorithms that have been developed by leading researchers to turn your data into actionable insights. Most importantly, when you use the Akkio AI platform, we handle processes like: Managing infrastructural requirements, deployment scalability and maintainability, data preprocessing, error handling, accuracy calculations, and more.
With Akkio, model training itself is entirely free, which means that you don’t have to risk anything during the process of experimentation. This is unlike traditional AutoML platforms, which charge for the training process itself, even if your models don’t pan out.
Beyond sales prediction, Akkio can even be used for product testing. To give one example, a large electronics brand was faced with thousands of pieces of feedback and survey responses, across a dozen test projects. To more efficiently manage this process, the brand turned to Akkio to build machine learning models that categorize and prioritize feedback.
Since Akkio is an AI platform that can learn from any structured data, it’s used in a diverse array of other areas, such as lead scoring, fraud detection, churn prediction, employee retention, and more.
Akkio lets you predict the future, based on past examples. Here's how.
Let’s say we give Akkio some historical lead data, with a column on whether or not that lead converted. We feed this into Akkio’s neural engine, and it will find patterns in leads that convert and those that don't. We can then use that model to generate a probability for any new lead that we feed it.
The model will produce a particular prediction for each new lead, and we can then display the predicted probability to our customer support or sales teams so that they can make an informed decision on whether or not they want to engage with this lead.
We can run this prediction through no-code tools like Zapier to create a notification for our sales or support teams. You can also directly connect the model to tools like Google Sheets, BigQuery, Hubspot, and Salesforce.
Deploying these models is crucial, as they can inform your marketing and sales strategies, giving you a competitive advantage by anticipating what your customers want before they know it themselves.
With Akkio, the complexities of deployment are taken out of the equation for you. You can start using our AI-enabled demand forecasting capabilities instantly, with no engineering implementation efforts or technical maintenance required.
Given simple data like historical sales volume and sales performance, you can get started with accurate time series forecasting. In moments, you can see predicted sales, and dive into individual data points, like predictions in one week or one month. Akkio’s predictors include a wide range of machine learning algorithms, and the most accurate forecasting model is used for any given dataset.
While traditional platforms like Kaggle would use data scientists to compete on AI solutions, our AutoML systems automatically pit different learning methods against each other to minimize error metrics, such as RMSE and mean absolute error.
To get started with Akkio, you simply need to sign up for a free trial.
Then, you can select an AI Flow as a demo, such as for demand forecasting or lead scoring, or create your own AI Flow. To get started with building a model for your use case, you'll need to have access to some historical data containing a column you'd like to predict.
Once you've got your data set built up, you'll want to start using Akkio’s no-code AI Flow. The AI Flow allows users to connect data, select the column to predict, build the model, and deploy, all in one simple interface. This allows users to build and deploy models in just a few minutes, all for free.
For example, in the interface below, we can see that a dataset of commodity price data was connected, and a model was built to forecast prices, simply by selecting the “average_price” column. While your dataset and column names may look different, the idea is the same: Connect a historical dataset and select the column to predict.
From here, you can deploy in any setting via our no-code Zapier integration, while more technical teams can use the Akkio API. We also have direct integrations to popular tools like Snowflake, Salesforce, HubSpot, and Airtable.
In this post, we’ve talked about how AI is changing the demand forecasting game. With AI, it’s now possible to create a more accurate model of your pipeline and plan for the future. And you can scale with models in the cloud, instead of on your own servers.
Akkio uses machine learning based on historical examples, which allows models to make predictions that are more accurate than previously possible. For most companies, having more accurate demand forecasting means they will be able to scale their sales operations and reduce their costs – making them more profitable businesses.