A Turning Point in Ecommerce: Forecasting and Budget Optimization Tools

1. Forecasting

If someone were to offer up a crystal ball to see into the future of your business, you’d jump at the chance to see it. While crystal balls don’t exist, forecasting tools can do the trick.

The biggest benefit of smart forecasting is that it allows your company to be proactive instead of reactive. It helps to set goals, create budgets, anticipate cash flow, and recognize changes within the market.

Here is what Agital’s forecasting tools enable you to glean insights into:

• Revenue Forecasts: Up-to-date revenue forecasts that include historical performance, recent performance, seasonality, and other variables to provide a reliable projection of future performance.

• Total Advertising Cost of Sale (TACoS) Forecast: What does your total ad cost-to-sale ratio look like in the future?

• Model Fit: Tracks how accurate your forecasting models are over time.

• Seasonality Coefficients: See how much seasonality impacts your business and how it can affect your revenue and performance. The higher the coefficient, the higher the impact seasonality has on revenue during that month.

2. Budget Optimization

Much to our chagrin, marketing budgets are limited, so brands are constantly challenged to use the budgets at their disposal in the most efficient way that delivers the biggest impact. Tackling this challenge without expensive custom modeling tools that take time and resources to configure is a tall order for businesses that have to prioritize immediate growth. To make it easy, we built a number of budget optimization functionalities into Agital’s BI platform.

First, it provides channel-specific campaign performance and optimization recommendations, informed by individual channel metrics, dashboards, and proprietary data interpretation.

This type of information is incredibly valuable, but it is still limited to a single-channel view of performance. What brands require to truly impact performance is a view of the big picture; a way to figure out how ALL of their marketing campaigns work together and contribute to overall business goals. Having this holistic view of performance enables brands to answer essential questions like what channels and campaigns they should invest more into and what should they cut back to achieve their business objectives. Put simply, brands need to answer the question:

“What is the optimal channel mix for my business, and how much money will I make if I spend X amount of dollars on it?”

That’s where media mix modeling (MMM) comes in.

MMM is an analytics and predictive modeling technique that enables marketers to measure how much each of their different channels contributes to their overall performance goals. The insights obtained from MMM allow marketers to refine their campaigns and budgets to create the ideal marketing mix. Because of the value delivered by MMM, such tools often cost thousands of dollars a month and require a data scientist on staff. At Agital, we wanted to make this functionality accessible to all, so we built a proprietary MMM tool into our BI platform. Here are some of the things our MMM tool can help brands assess:

Hypothetical Ad Spend

Will increasing spend in paid search campaigns over social ones impact sales? How about adjusting spend on product ads? This functionality enables you to experiment with different ad spend allocation across channels to see how it will impact future revenue.

Revenue Projections

Media mix modeling (MMM) enables brands to understand performance history and look through the lens of future revenue with and without channel optimization. This functionality shows you revenue forecasts based on your current ad spend as well as your future, optimized ad spend.

Spend more, reallocate budget, or both?

MMM allows you to put an end to spending on gut feelings and know for sure which channel mix and budget allocation produce the highest revenue. A Channel Spend Opportunities breakdown will use advanced modeling to show how much you’re getting in return for every dollar spent in a particular channel. The example below shows that—for this brand—every dollar spent in Product Ads generates $12.4, every dollar spend on Paid Search drives $7.35 and every dollar spent on social has the same $1-return. Based on this data, a brand might consider a shift in budget from social toward product ads.

See the Full, Cross-Channel Picture

MMM can help you identify if and when to move budgets to better-performing channels. Having detailed breakdowns of your spend across channels in one place means you won’t lose sight of your full advertising mix and can understand how everything is trending at a glance.

While media mix modeling can sound like a complicated concept, it can also be surprisingly simple to implement. Take a look at this media mix modeling case study to learn how this fashion retailer increased sales by 40% without increasing ad spend.

3. Customer Acquisition

There are a lot of ways we can look at customer acquisition, but at the center of it all is customer lifetime value (CLV). If you do not have a way to measure the lifetime value of the customers you have or are trying to acquire, your strategy is missing its most critical component. That component alone can define your revenue, budgets, channel strategies, goals, and profitability.

All ecommerce businesses should know their customer lifetime value and look to optimize it. CLV is essential in measuring customer loyalty as well as the long-term financial viability of your business.

CLV is instrumental in:

• Acquiring More Customers: Knowing how much a customer is worth over time allows you to set proper new customer acquisition goals, realistic budgets, and accurate cost-per-acquisition targets to reach those goals. If you are not factoring in CLV within your bid strategies, your target cost per acquisition is likely too low and you’re missing out on customers you would have otherwise been able to profitably acquire.
• Revenue & Brand Growth: If you understand your CLV, you can set better cost per customer acquisition (CPA) targets. A CPA target that accounts for customer lifetime value will be higher than a target informed only by the value of a first transaction. A higher acceptable CPA will allow you to be more aggressive with your marketing and reach more customers more effectively. This, in turn, means that you’ll be able to attract a larger customer base that drives more revenue, more consistently.
• Remaining Competitive: In a competitive landscape, it is critical to understand how much you can spend to profitably acquire a new customer. Understanding CLV instead of return on investment (ROI) on a single sale or campaign will allow you to stay competitive in rising CPC environments and give you the ability to bid more. For example, if you sell a $50 item, but know that the sale of that $50 item is actually worth $120 over the lifetime of the customer, you can bid more to acquire that initial sale, leading to an overall net gain in sales/customers. It can allow you to take an even larger share of your market vs competitors who are not considering CLV within their marketing strategy.
• Increasing Cash Flow: With a larger, more diverse set of customers, you can increase your cash flow—allowing for more opportunity, flexibility, and less risk for your business investments and operations.
• Better Projections & Planning: An understanding of your CLV allows for more accurate revenue projections. Better projections result in more informed and more reliable marketing investment plans and forecasts.

Below are some of the key CLV metrics and functionalities (all conveniently available in Agital’s BI platform) that brands can use to better understand the value of their customers and make strategic decisions accordingly.

Key CLV Metrics: Instantly view the key metrics associated with CLV and compare them over time to assess the health of your business, make accurate revenue projections, and define performance.

CLV Cohorts: Access the CLV data from different customer cohorts. This information can be used to adjust future strategy—for example, the data below shows us that customers acquired in April drive a higher CLV than average. This could mean a higher ad budget and a more aggressive ad strategy in April. Or, you may investigate why customers acquired in April had a higher CLV in the first place, finding out, for example, that a certain sub-set of products drove increased sales during that month and that those products generated repeat customers at better rates. With this knowledge in hand, you can loosen your target CPA for those products knowing they have a higher associated LTV.

Changes Over Time: See how your CLV changes over time to help you develop effective retention and upsell strategies for each of your customer segments.

CPA Goal Setting: Use CLV data and margin data to help set informed cost per acquisition targets, identify break-even points, and build toward increased profitability.

New Customer + Total Customer Trends: Track how many total customers you acquire in a given timeframe, how many of them are new vs returning, and the revenue contributed by each segment. This data will give you true insight into your most valuable customers and help you define more effective strategies to attract and nurture them.

Conclusion

At Agital, we want our clients to have access to tools that deliver accurate forecasting, help with smart budget allocation, and measure customer lifetime value without having to invest thousands of dollars every month to get actionable insights. We are thrilled to introduce such advanced, enterprise-level capabilities into our BI platform.