Everyone wishes they could see into the future, right? If only we possessed some sort of magical power that allowed us to step forward in time and observe, we’d be able to anticipate every peak or valley our industry experienced before it actually happened.
Just take a second and consider what you would do given a 6-month head start on the post-lockdown sales environment of 2020/2021. Maybe you’d invest in streamlining your website for eCommerce, lean into new avenues for digital engagement, and pioneer the virtual tasting experience. With that kind of infrastructure already in place, you could have skipped the panic phase after temporarily shutting down your tasting room and jumped to the front of the market in an instant. Now THAT’S how you get into Cooperstown (or its wine industry equivalent).
With time travel or an unexpected blessing from the gods off the table though, is this foresight even remotely achievable? As a matter of fact, there’s a practice that’s already deeply engrained in our society today designed for this very purpose: forecasting.
Now, before we jump to conclusions here, the chances of predicting a monumental shift in the market like we saw in 2020 are wildly slim. On a more localized scale though, when looking at very specific aspects of your winery’s performance, forecasting allows you to see what the future might have in store for you. All you need to get started is a combination of these three key components:
Data
Math
Experience
As we continue, I’m assuming you have at least some awareness of forecasting as a concept to start. My analysis of each blog post’s performance shows pretty clearly that article titles serve to drive the vast majority of engagement I see week to week, and I’ve already succeeded in soliciting your click today. With a title like “The Winery Guide To Forecasting”, I’d say we can put two and two together with a decent amount of confidence.
That being said, the goal is to provide you with a roadmap to either start your individual forecasting journey or build on what you already have in place. To do that, we’ll look at 3 different levels of forecasting difficulty:
Beginner
Intermediate
Advanced
For each, I’ll provide a weighted scale of how much data, math, and experience you’ll need in order to achieve an actionable result out of 10.
Let’s get started!
Beginner
Data: 1 / Math: 1 / Experience: 8
As a leader in your organization, who’s very much attuned to the cycles and trends your winery experiences, you’re already more than able to create generally accurate forecasts. You know that tourism will boost business during the summer when the weather is nice, you lose the highest number of club members right before you charge them for their allocations, and you need to produce a minimum amount of your signature label in order to fulfill demand. This is all knowledge gained from experience and serves you well during the forecasting process.
The best place to start is by mapping out a general outlook for the upcoming year via categorizing each month’s traditional sales trend. Personally, I like to use low/normal/high, and it may look something like this:
I know that I:
Traditionally sell less in the early winter months of the year
Run my club in March, which translates to a spike in sales
See an uptick throughout the summer due to tourism
Run my second club in October for another sales spike
Have a successful holiday gifting program in December that customers love
Once you have this set, double check that your assessments are correct by pulling last year’s sales data and calculate your total revenue for each month. If both your assessments and the calculated revenue are generally aligned, you now have a forecast you can work with. Rudimentary as it may be, this forecast will serve to manage your expectations from one month to the next. It also gives you a deeper insight into your business if/when the sales deviate from what you expected.
Intermediate
Data: 2 / Math: 3 / Experience: 5
Having a general understanding of your winery’s business expectations is good, but a general forecast will never give you more than a generic outlook to plan around. In a world of shifting markets created by thousands of outside factors, the ability to narrow down your predictions to a more finite level will give you even more of an edge as you strategize your next move. With the industry still heavily reliant on wine clubs as a key revenue driver year over year, having an informed idea of what that population looks like each month is undoubtably a key factor in any winery’s strategic planning process.
For this round, we’ll start by pulling 3 years of club population data. I like using 3 years of data in this instance because it really helps to illustrate what’s happening in the club over time. With countless factors in play for each club member, there’s always a possibility of a random significant positive or negative swing taking place that will throw off your results (which, when represented within the larger dataset, are called outliers).
Example:
Let’s say we’re attempting to forecast club population growth based on 1 years’ worth of monthly changes from 2020. Assuming our starting population was somewhere around 300 total members, our data might look something like this:
Yikes… Even though we charge customers for their allocation in April, which has traditionally resulted in a higher than average cancellation rate, those population losses don’t feel right.
Let’s compare that to what we might see over 3 years:
Now, those massive changes from March – May stick out like a sore thumb as outliers and lead us to believe that something else may have been at play during the time period to cause them. Not to fear, we can easily account for this in our final forecast; In retrospect though, by limiting the scope of the data we used, our forecast would have been about as useful as corkscrew without a handle.
I always repeat this little mantra to myself when searching for outliers, and so far it’s served me well: once is a fluke, twice is a coincidence, and three times represents a trend. Unless you see a big shift repeated year over year over year, you’re fairly safe to treat it as an outlier.
After identifying our outliers, we can now create our intermediate forecast by calculating the average change for each month. Devotees of this blog will remember how to do that from my previous explanation detailed here; but for the uninitiated, that entails adding your values together and dividing by the number of values you added.
Example:
Focusing on July:
Calculation: 8 + 9 + 10 = 27
Average: 27 / 3 = 9
This gives us an average population growth of +9.
When dealing with a month containing an outlier, we’ll remove that value from the equation altogether. For April:
Calculation: (-14) + (-16) = -30
Average: -30 / 2 = -15
In the end, after accounting for our outliers in red below, we’re left with the following forecast:
From here, you can tweak the forecast in different ways to more accurately represent what you believe will happen based on your experience.
Let’s say your gut is telling you that the wine club will continue to grow by 10% into next year; you can add a percentage growth factor to each forecasted number to reflect that. Now, instead of +10 in November, you’d add an additional 10% on top to give you a forecasted growth of +11.
Maybe you bring on additional tasting room staff over the summer and you want to start incentivizing them to sign up more members. Give them an ambitious goal of 30% growth over the three month period from June – August, plan for a more realistic 20%, then add your 20% growth factor into your forecast for those months to give you a growth of +9.996, +10.8, and +15.24 respectively.
Your experience dictates the strategy, which can then be reflected in the forecast and utilized. It’s all about providing yourself with the most plausible outcome, so you can then use your forecast as a guide for further strategic planning.
Advanced
Data: 8 / Math: 8 / Experience: 2
So, you've dipped your toes into the world of forecasting and you're ready to dive into the deep end. Welcome to the advanced level, where data is king, math is your trusted advisor, and experience (while still valuable) takes a bit of a backseat. Here, we harness the power of sophisticated statistical models and predictive analytics to peer into the future with greater precision than ever before.
At this stage, you'll be dealing with large datasets and complex mathematical models. But fear not! With the right tools and a bit of perseverance, you'll unlock insights that can transform your winery's strategic planning. Let's break down what this entails.
First things first: data, and lots of it. We're talking about gathering and analyzing multiple years' worth of information across various aspects of your business and the industry at large. This includes:
Historical Sales Data: At least five years to capture long-term trends
Customer Behavior: Purchase history, club membership duration, and engagement metrics
Market Trends: Industry reports, competitor analysis, and economic indicators
External Factors: Weather patterns, tourism statistics, and even social media sentiment
The goal is to create a comprehensive dataset that reflects all the variables influencing your winery's performance.
With your data in hand, it's time to delve into advanced statistical techniques. Here are some methods you might employ:
Time Series Analysis
This involves analyzing your data points collected (or sequenced) over time to identify trends, seasonal patterns, and cyclical fluctuations. Models like ARIMA (AutoRegressive Integrated Moving Average) or Exponential Smoothing can help forecast future values based on past behavior.
Example:
Imagine you've plotted your monthly sales over the past five years and noticed consistent peaks during the summer and winter holidays. Using time series analysis, you can model these seasonal effects to forecast sales for the upcoming year with a higher degree of accuracy.
Regression Analysis
Regression models help you understand the relationship between your dependent variable (like sales) and one or more independent variables (like marketing spend, tourist footfall, or even average temperature).
Example:
Suppose you suspect that your sales are heavily influenced by local tourism and your advertising efforts. By running a multiple regression analysis, you might find that:
For every $1,000 spent on advertising, sales increase by $5,000
For every 100 tourists visiting the area, sales increase by $2,000
This quantifiable insight allows you to allocate resources more effectively.
Machine Learning and AI
For the tech-savvy, machine learning algorithms can handle complex, nonlinear relationships in your data. Techniques like Random Forests, Neural Networks, or Support Vector Machines can uncover patterns that traditional models might miss.
Example:
Using machine learning, you might discover that social media engagement levels predict tasting room visits with a 90% accuracy rate. Armed with this knowledge, you can ramp up your online activities during key periods to boost foot traffic.
Scenario Planning and Simulations
Advanced forecasting isn't just about a single prediction; it's about exploring multiple "what-if" scenarios to prepare for various possibilities.
Monte Carlo Simulations: Use random sampling and statistical modeling to estimate possible outcomes of an uncertain event
Sensitivity Analysis: Assess how different values of an independent variable affect a particular dependent variable under a given set of assumptions
Example:
By simulating thousands of scenarios with varying levels of tourism and economic conditions, you can estimate the range of possible sales figures and prepare contingency plans accordingly.
Bringing It All Together
Let's construct a simplified example to illustrate how advanced forecasting might look:
Step 1: Data Compilation
You've gathered five years of monthly sales data, along with variables like:
Marketing Spend
Tourist Numbers
Average Monthly Temperature
Social Media Engagement Metrics
Step 2: Model Selection
After exploring your data, you decide to use a combination of time series analysis and multiple regression.
Step 3: Building the Model
Time Series Component: Captures the overall trend and seasonal patterns in your sales data
Regression Component: Quantifies the impact of external variables like marketing spend and tourist numbers
Step 4: Validation
You test your model against a portion of historical data to see how well it predicts known outcomes, adjusting as necessary to improve accuracy.
Step 5: Forecasting
With a validated model, you generate forecasts for the next 12 months that look something like this:
Step 6: Scenario Analysis
You create scenarios based on different assumptions:
Optimistic Scenario: Higher tourist numbers and increased marketing spend
Pessimistic Scenario: Economic downturn leading to reduced consumer spending
Most Likely Scenario: Continuation of current trends
Each scenario adjusts the input variables accordingly, giving you a range of potential outcomes to plan for.
Tips for Success:
Data Quality is Crucial
Garbage in, garbage out. Ensure your data is clean, consistent, and as comprehensive as possible.
Stay Curious
The more you explore your data, the more insights you'll uncover. Look for patterns, correlations, and anomalies.
Collaborate with Experts
If statistical modeling isn't your forte, consider partnering with a data scientist or consultant who can bring technical expertise to the table.
Iterate and Refine
Forecasting is an ongoing process. Regularly update your models with new data and adjust for changing conditions.
Communicate Findings Clearly
Use data visualization to make your forecasts understandable to stakeholders. Clear graphs and charts can convey complex information effectively.
The Payoff
By embracing advanced forecasting techniques, you're positioning your winery to make data-driven decisions that can significantly impact your bottom line. Benefits include:
Optimized Inventory Management: Reduce waste by aligning production with demand forecasts
Strategic Marketing: Allocate your budget to channels and times that yield the highest ROI
Enhanced Financial Planning: More accurate revenue projections support better budgeting and investment decisions
Competitive Advantage: Stay ahead of industry trends and respond proactively to market shifts
Final Thoughts
Advanced forecasting transforms the daunting task of predicting the future into a structured, data-driven process. While it requires a substantial investment in data collection and analysis, the insights gained are invaluable.
Remember, even the most sophisticated models have limitations. They are tools to inform your decisions, not digital psychics. Combine them with your industry knowledge and intuition for the best results.
Forecasting is a journey, not a destination. Whether you're mapping out general trends or delving into complex statistical models, each step you take enhances your understanding of your business and the market.
So, here's to embracing the power of data, sharpening your mathematical skills, and continuing to build on your experience. The future may be uncertain, but with robust forecasting, you'll be better prepared to navigate whatever comes your way.
Cheers to a well-planned and prosperous future!
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