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4 Tips to Build a High Impact Forecasting Model for your Startup

While it may seem that startups are moving through their growth plans almost by accident, making quick decisions to try and keep up with trends…they aren’t. Ok, some are, but the successful and well led startups aren’t. Today, we’re diving into our 4 top tips for building a high impact forecasting model.

Forecasting can be applied to every decision a startup makes. In this way, a startup ensures that it’s never in a position where it finds itself unsure or having to be reactive. If business strategies are supported by insightful data and predicted outcomes, the initiative is weighted towards the startup and each move is a deliberate step forward towards a larger goal.

In forecasting, accuracy is key. Following your gut is not the right way to secure your next funding round! Obviously, each startup wants to have high impact forecasting models that can support decision making, this blog will help with that. Continue reading below for our top tips.


Forecasting models

What are they?

A startup may use forecasting models to help them develop their business strategies.

Data is collected and analysed so that patterns can be identified. The data will be a mixture of current and historical. The impact of big data and artificial intelligence has been transformational on forecasting strategies. There are many different techniques that a startup may use to create a forecast. All of these methods fall into one of two approaches: qualitative and quantitative.

There are too many forecasting approaches to cover in this blog, and the statistical and mathematical theories underpinning them would make for very dry reading, but there are common themes that forecasting models have.

A situation is chosen/described

For example, this may be along the lines of “will our premium product range sell in sufficient numbers” or “what will revenues be in 12 months’ time”.

The scope and limits of relevant data are chosen

The forecaster or team sets the scope of the forecasting model. The relevant data variables are identified and decisions are made on how to collect/retrieve the data.

Assumptions

Simplifying boundary conditions and contexts are applied in order to make data retrieval more straightforward.

Pick a model

The forecaster or team then decides on the forecasting model that best suits the dataset.

Time to analyse

The data is analysed and the forecast is made off the back of it.

Check your work

The model is compared to what happens in actuality, allowing updates to be made. Variables and assumptions may be changed in order to improve the model and make future forecasting more accurate.


4 Tips to build a high impact forecasting model

Now we’ve discovered what forecasting models are, how do we ensure our forecasts are high impact? Here are our four tips.

1. Data, data, data

Data is king when forecasting in business. If the data is irrelevant the model will not be able to perform as intended. A forecasting strategy will need to answer the following questions.

Which data variables are relevant?

Where will this data be collected from?

Who will retrieve it?

How will it be collected?

Each of these questions is an opportunity to get things right and reduce the risk of inaccurate forecasting.

The relevance of the data variables is important, as if you introduce data that are meaningless to your forecasting, it may have a negative impact. At best, if the data is irrelevant, then there will be a waste of resource retrieving and checking it. At worst, the data may cause the model to fail and provide outputs that are not only essentially meaningless (based on irrelevant data), but they may also cause the startup leaders to make poor business decisions.

Once you know what data you want to collect, you need to know where to get it. How is this data retained in the business? Are these systems compatible with the model you want to use, or will there need to be some data transfer or manipulation performed?

Pulling data from IT systems can be trickier than it sounds, in some cases, software may need to be employed, or even created, to extract the relevant data variables - particularly if more than one IT system is used across the startup. This can lead to responsibility being placed on one or two key individuals, which creates a risk of having nobody with the expertise to check the extraction has been performed correctly.

Any forecasting strategy put together should give thought to these issues and take steps to reduce the risks.


2. Don’t lose hope, but keep it real

When forecasting is performed at a startup, it might be tempting to be swayed by the prevailing business context at the time. If things are going well, it may seem entirely obvious that things will continue to go well, ad infinitum.

However, although we can only base future predictions based on current data, we need to be careful that a level of unconscious bias doesn’t creep in.

Care needs to be taken that predictions aren’t overly pessimistic, which may result in business leaders deciding against dedicating resources to projects with little forecasted upside. At the same time, overly optimistic assumptions can lead to resources being wasted on projects that have little impact.

Start each forecasting model with a clean slate. Take off the rose tinted glasses and be as grounded and level headed in your assumptions as possible.

Stay realistic.


How to be an effective director

Founders and business leaders are an instrumental part of any company structure. With an ever increasing trend for businesses to appoint multiple directors across a range of functions, this can make it challenging to fully understand roles and responsibilities. Fortunately, School of Startups is here to help. Check out our guide on how to be an effective director, today.

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3. Review regularly

Forecasting is a valuable tool and when done correctly can inform business strategies and provide valuable insights into almost every aspect of a business's operations.

But even when it is done right, there’s no telling when an established model might go out of date or become ineffective due to changes in data, market conditions, or consumer behaviour. Forecasting models should be reviewed regularly to ensure assumptions and datasets remain relevant. These reviews should investigate how improvements could be made, and this knowledge should be shared across the team to ensure future models start from a position of strength.

The total sum of all current knowledge should be the starting point for each forecasting model. The review process should never stop.

4. Understand desired outcomes

Forecasting should be a deliberate effort, performed to inform a specific decision or situation. The assumptions and boundaries of the model should be limited and easy to follow.

With this in mind, the forecasting model should be chosen with the outcome in mind.

What insights do the business leaders need? Will the chosen model provide an output that is in line with the goals of the project?

Understanding the desired outcomes doesn’t mean tailoring the model to provide a specific output. But it does mean tailoring the model so that it can provide a range of outputs in the type/format that business leaders need to be able to inform their strategy.

A strong and effective forecasting model doesn’t just tell founders and managers what they want to hear. The output of any model should be useful, whether it has a positive or negative impact. So when founders ask a question in English, they don’t want the answer to be in Chinese.


In summary

  1. Forecasting can be dangerous. They can become a focus of businesses and lead to activities and resources being limited as the future may be perceived as ‘predetermined’.

  2. Review regularly. It’s important that any models are discussed and reviewed regularly, and that the decision makers relying on them fully understand the assumptions and limitations they’re based on.

  3. Remember the risks. Forecasting models use known data to make informed predictions about business decisions or future market conditions. There are risks involved when relying on historical data and assumptions about the future, these risks should be discussed as part of the forecasting process, but they cannot be fully removed.

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