Revenue Plan/Forecast

Introduction to Forecasting (Revenue Plan & Marketing Plan)

Summary: This article provides an introduction to forecasting in Daasity, focusing on how you can input your Revenue Plan and Marketing Budget/Spend forecasts into the platform and use them to track performance vs. plan. We’ll explain where the forecast data comes from – whether you enter it manually via Daasity’s Brand Supplied Data sheets or have it auto-fed – and how that data flows into the Daasity data model. Finally, we’ll point out where you can view the forecast vs. actual results in your dashboards and reports. By integrating your targets (sales targets, marketing spend targets) into Daasity, you get powerful “plan vs. actual” insights to help you course-correct and hit your goals.

Overview of Forecasting in Daasity

Forecasting in Daasity is all about comparing what you planned to what actually happened. The platform allows you to load two main types of plan data:

• E-commerce Revenue Plan – your projected online sales and related web metrics (orders, units, sessions, new customers).

• Marketing Plan (Budget/Spend) – your planned marketing spend and efficiency metrics (budget by channel, target CPA, target ROAS, etc.).

These plans are typically input on a monthly or daily basis for future periods (e.g., you might plan out each month’s sales for the year, or each day’s sales for the upcoming month). Once input, Daasity’s model ingests the data and makes it available in explorations alongside actual performance data coming from your integrations (e.g., Shopify actual sales, Google Analytics actual sessions, Facebook actual spend).

By doing this, Daasity can power plan vs. actual reports – for instance, you can see that for the month of September, you planned $500k in revenue but only achieved $450k (90% to plan), or that you intended to spend $50k on Facebook but actually spent $60k. These comparisons help identify areas ahead or behind plan.

Forecast data can be entered manually via a Google Sheet (the BSD Forecast sheets) or, for some managed clients, automatically pulled from another source if you maintain forecasts elsewhere (like maybe an Anaplan or spreadsheet integration – this is less common, but possible with custom work). Most customers, however, will update their forecasts in Daasity’s provided template sheets.

Now, let’s break down the two main components: Revenue Plan and Marketing Budget/Spend.

Revenue Plan: Projecting Your Sales and Site Metrics

The Revenue Plan is where you input your expected sales and traffic for future periods. It usually lives in a tab called “Revenue Plan” in your BSD Google Sheet . Here’s how to use it:

• Metrics in Revenue Plan: The sheet typically has columns for Date, Store, Revenue (Sales), Orders, Units, Sessions, and New Customers (and perhaps Currency). You fill in each row with a date (or month) and the corresponding projections for those metrics . For example, you might say for January 1, 2024 (Date) for Store “US Webstore” (Store), you plan $10,000 Revenue, 200 Orders, 500 Units, 8,000 Sessions, 150 New Customers.

• Daily vs Monthly Planning: You can choose to plan at a daily level or monthly level. There may be a “Plan Format” or “Budget Period” selection . If you choose daily, you enter each day’s values. If monthly, you might enter one row for the month (e.g., Date = 2024-01-01 with Revenue = $300k meaning the month of January plan is $300k). Daasity will then typically spread or compare that appropriately – for instance, in daily comparisons, a monthly figure might be divided by days for an average daily plan . It’s crucial not to mix the two in one section: stick to one format for consistency.

• Multiple Stores: If you have multiple storefronts (e.g., US, EU, Retail, etc.), the plan is often broken out by store. The “Store” column lets you specify which entity the plan is for . You might have one plan row for “US” store, another for “Canada”, etc., for the same date. There’s also often an “Other” store option, which corresponds to the “Other Order Source” in BSD (for any miscellaneous sources of sales you track) .

• Filling it Out: Enter your projected values for each required field. Not all fields may be required, but typically Revenue is required, and others can be optional if you don’t forecast them all . However, it’s good practice to fill what you have. If you only care about revenue plan, you could fill just revenue and perhaps orders; the model can still compute a revenue vs actual gap. But if you also plan traffic (sessions) and new customers, you’ll get the benefit of comparing those too.

• No Blanks or Extra Columns: Follow the instructions on the sheet about not adding columns or leaving gaps . Each row should be complete for the fields you are using – otherwise the import might skip that row.

Once you input the data and it’s saved in the sheet, Daasity’s pipeline will ingest it (usually on the next warehouse refresh). After that, you can find the data in:

• The Daily Plan vs Actual Explore – this will have “Planned Revenue”, “Planned Orders”, etc., alongside actuals.

• The Flash vs Plan dashboards – Daasity often provides a Daily Flash vs Plan dashboard that shows actual daily sales vs your plan, with variance.

• Any custom reports you build: you can pull in planned metrics to compare. For example, an email performance dashboard could include a tile for “Sessions vs Plan” if you filter to email channel, assuming you had planned traffic for that channel (though usually plan is at higher level).

How Forecast Data Flows into the Model

Understanding behind the scenes: once you input plan data, Daasity’s ETL processes pick it up:

• The data from Revenue Plan and Marketing Budget/Spend sheets is stored in specific tables in the data warehouse (often called something like bsd_revenue_plan, bsd_marketing_budget, etc.).

• During the daily transformation, there are steps (like the “Daily Plan Code” and “Plan to Actual Code”) that merge this BSD data with actuals . For example, Plan to Actual Code will create comparisons for each date and metric of plan vs actual.

• Metrics like “Plan Revenue” or “Plan Spend” become available as LookML measures. They pull from those BSD tables.

• If you provided monthly plans, the code will disaggregate or align those with daily actuals properly (e.g., dividing by days for daily comparisons, as mentioned).

• The model also often creates variance measures (like Plan vs Actual % or difference) which you may see in explores (e.g., “Revenue Variance % = (Actual – Plan) / Plan”).

For customers with automated forecast ingestion: some enterprise companies might maintain their forecast in another system. In theory, instead of typing into the BSD Google Sheet, they might have a process where Daasity fetches a CSV from their system or even a direct integration. If that’s the case, the end result is the same – it populates those plan tables. The difference is who/what is writing to them (you via Google Sheet vs an automated feed). From a user perspective, you might not even notice if it’s automated, except that you don’t have to manually update the sheet.

Viewing Plan vs Actual Results

Where do you actually see the fruits of this labor? A few key places in Daasity to check:

• Flash vs Plan Dashboard: Often titled “Daily Flash vs Plan” or similar. This dashboard might show a chart of daily revenue actual vs plan, perhaps a bar for each day (with plan as a line or bar and actual as another), highlighting differences. It likely also has summary KPIs like “Month-to-Date Actual vs Plan” with percentages. If you’ve entered Orders, New Customers, etc., those might be on there too. This is your go-to for a quick health check: are we ahead or behind plan today, this week, this month?

• Marketing Performance vs Plan: Some dashboards focus on marketing and will use the marketing budget data. For example, a “Marketing Plan vs Actual” might show by channel: Planned spend vs Actual spend, Planned ROAS vs Actual ROAS, etc. If not a standalone dashboard, these comparisons might be integrated into the main marketing dashboard (e.g., showing a column “Planned Spend” next to actual spend in a table of marketing channels).

• Explores:

  • Daily Plan to Actual Explore – allows you to build custom reports on any of the planned metrics vs actual. For instance, you could filter to a particular month to see daily plan/actual for that month. Or break it by store if you had multiple stores planned.

  • Daily Marketing Plan to Actual Explore – similar idea but focusing on marketing, possibly breaking out by channel/vendor .

  • These explores provide raw data access: you might pull “Date, Revenue, Planned Revenue, Revenue Variance %” for a period to see trend. Or “Channel, Actual Spend, Planned Spend, Variance” to see where you’re over or under-investing.

• Variance Alerts: If you use Daasity’s notification system, you could potentially set up alerts (if supported) like “notify me if daily revenue falls 20% below plan”. This isn’t an out-of-box thing but something you could achieve via Looker’s alerts if you have access. At the very least, you can manually monitor or use conditional formatting in a Look to highlight variances beyond a threshold.

By regularly consulting these, you can take action: e.g., if revenue is trending below plan for the week, perhaps you’ll increase marketing spend (or adjust the plan!). If a certain channel is overspending versus budget without the expected return, you might reallocate budget.

Standard vs. Managed Forecasting

For most (standard) users:

• You manually maintain the plan in Daasity’s provided sheets. It’s a bit of manual work, but many find it useful to do monthly (plan your next month’s daily forecast at the end of the prior month, and keep rolling).

• You update it when plans change (if there’s a mid-month goal revision, you can adjust the sheet).

• The onus is on you to ensure the plan is up to date – Daasity will surface whatever is in the sheet.

For managed/enterprise:

• If you have a more complex planning process (like an internal financial model), Daasity might help automate the ingestion. For example, they could connect to a Google Sheet that your FP&A team uses, or set up a process where you email a spreadsheet and it loads. In some cases, if you use a tool like Adaptive or Anaplan, you might export from there and feed Daasity.

• The Daasity team can also help with forecasting models – e.g., you might ask “Can we incorporate a baseline forecast that automatically projects next month based on last year’s data?” That gets into predictive modeling, which is beyond a simple input, but an interesting advanced customization. Some companies do build predictive forecasts externally and just load the result as the plan.

• Regardless, the model handling and dashboards remain the same. It’s mostly about how the data gets into the plan tables (manual vs automated).

Tips for Effective Use of Forecasting in Daasity

• Keep Plans Realistic and Updated: The usefulness of plan vs actual analysis depends on your plan quality. If you put in overly optimistic numbers that you never update, the variance won’t be actionable (“oh look, we’re at 50% of plan every month, but that plan was a fantasy.”). Try to update forecasts when major business assumptions change, so you’re always comparing against a realistic baseline. Some teams update the plan monthly or quarterly based on latest trends.

• Use Plan vs Actual for Daily Standups: Many DTC brands have a daily or weekly standup on performance. Having the flash report with plan comparisons is great for these meetings. It quickly answers “are we on track?”. You can then dive into specifics (maybe drill down by channel or by category to see what’s causing a shortfall).

• Plan at a useful granularity: If daily volatility is high and it distracts you, consider planning at monthly level in the sheet. Daasity will still allow daily actual vs a prorated daily plan. But you won’t overreact to one day missing plan if the monthly is on track. Conversely, if seasonality or events matter (e.g., you expect a big spike on Black Friday), plan daily around those events – it will yield more insightful variance analysis (e.g., “Black Friday beat plan by 10%, but Cyber Monday fell short”).

• Include All Major Channels in Marketing Plan: To get full visibility, include budgets for all significant marketing channels in the Marketing Budget sheet, even if some are flat or zero. If you only plan Facebook and Google but ignore Email (assuming cost $0 but it drives sales), your plan vs actual on new customers might be off because you got more from “free” channels than planned. It’s okay if some channels have no “spend” but you might still have a plan for their outcomes (like plan for Email to contribute X sessions or revenue, even if spend is $0). Currently, Daasity’s marketing plan template is spend-focused, but you can implicitly cover non-spend channels by forecasting their outcomes in the revenue plan (sessions/new customers) if needed.

• Look at Both % and Absolute Variances: A dashboard will often show percentage to plan (e.g., 95% of plan, or 105% of plan). While percentages are good for relative view, always check the absolute difference too (we’re $50k short of plan – is that a big problem or can a single promotion make it up?). Similarly, small variances in big metrics vs big variances in small metrics warrant different responses.

In summary, by feeding your targets into Daasity, you turn it into not just a reporting tool but a performance management tool. It holds you accountable to your goals everyday. And because everything is in one place, you can trace variance to its root cause (since you have all channel and product data at your fingertips to investigate why something missed the mark). It’s a powerful practice to adopt for data-driven teams.

Relevant Articles:

• Revenue Plan (step-by-step guide to filling the sheet)

• Marketing Budget (guide to filling marketing plan sheet)

• Daily Flash vs Plan Dashboard (overview of how to use the flash report)

• Plan to Actual Explore (reference on exploring plan vs actual metrics)