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Welcome to the Thrive in Fashion
podcast where we explore the 

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role of the fashion buyer, the 
knowledge and skill sets 

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required for success and the 
impact of the role in your 

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retail business. 
In each episode you will learn 

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about industry best practise and
how the buyer's role is evolving

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in this ever changing, 
fast-paced industry. 

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And now here is your host, a 
fashion industry expert with 

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over 20 years of experience, 
Elizabeth McHale. 

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Welcome to this week's episode 
of the Thrive in Fashion 

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podcast. 
I'm Elizabeth McHale, and today 

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we are talking about 
forecasting. 

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Forecasting is one of the most 
practically useful skills in 

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fashion buying. 
It's another area that's not 

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formally trained. 
Most buyers move through their 

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early careers making decisions 
based on a mix of market feel, 

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previous season's performance 
and what their manager or 

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merchandiser tells them the plan
is. 

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If I was to ask you, when you 
are deciding how much of a 

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product to buy, how many units 
across how many options, in what

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size ratio, where does that 
number come from? 

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For most buyers, especially in 
the first few years of the role,

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the honest answer involves some 
version of, well, what we did 

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last season, or what my 
merchandiser suggested, or what 

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felt right when I was looking at
the market and a rough sense of 

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what the customer goes for. 
All of these are good and all 

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are starting points, but none 
are forecasting. 

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Forecasting is not the same as 
trend research, although trend 

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research feeds into it. 
It's not the same as reading a 

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sales plan, although that also 
feeds into it. 

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Forecasting is the discipline of
taking what you know. 

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So that's taking information 
from your sales data, 

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information from your market 
research, from historical 

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performance, and then 
translating that into a plan 

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that drives your buying 
decisions with commercial logic 

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rather than just instinct alone.
And the reason I'm talking about

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it this week is because it also 
relates to the previous episodes

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where I spoke about OTB planning
and margin. 

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As I mentioned, forecasting is 
not trend prediction. 

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Trend prediction is about 
identifying what will be 

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aesthetically relevant, what 
direction products should go in,

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what the customer will want to 
wear. 

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That is a creative and market 
intelligence exercise, and it is

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genuinely important. 
But it is not forecasting. 

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Forecasting is about quantity 
and timing. 

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How much of something will sell,
when will it sell, and at what 

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rate. 
So the two activities are 

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related, but they're not 
necessarily the same skill. 

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Forecasting is also not the 
sales plan your merchandiser 

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presents to you. 
The sales plan sets the 

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financial target. 
Your forecast or estimate is 

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your own view of how the 
products you were buying will 

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perform against that target, 
which lines will drive volume, 

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which will underperform, where 
you might need more depth and 

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where you can afford to go 
narrower. 

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Your forecast is your commercial
perspective, informed by data 

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and most of the time made when 
looking at your OTB because in a

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lot of cases your planning and 
budgets and targets are set well

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in advance of your trading 
season. 

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It's only when you get into 
season that different things can

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unravel and things can change. 
So your assumptions or your 

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forecasts are a really good way 
of tightening up on what the 

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figures are and ensuring that 
you're still controlling your 

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stock figure while achieving 
your budgeted sales. 

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Your forecast is your commercial
perspective, which is informed 

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by data and the sales plan is 
the target that you're working 

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towards. 
In practical terms, a buyer who 

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is forecasting is asking 
questions like based on last 

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season's performance on this 
product type, what sell through 

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rate should I plan for? 
If I buy 500 units of this style

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and my expected sell through at 
full price is 65%, what markdown

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should I budget in for the 
remaining 35 and will that 

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impact my margin and if so, do I
need to adjust the depth of my 

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buy to compensate? 
These are the type of questions 

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that you automatically ask when 
forecasting is a consistent 

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practise of asking the right 
commercial questions before any 

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commitment is made. 
Forecasting or assumptions are 

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also done in season, 
particularly when discussing 

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OTB, because a lot of the 
decisions that you made in 

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advance may not materialise as 
you expect. 

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Therefore, when you're trading 
in season, it's really important

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to go back again and relook 
every time you're having an OTB 

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meeting but what your budget is 
and what your sales are. 

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And look at what assumptions you
might apply given the new 

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information or the current 
context of trading patterns to 

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see how much that will impact 
either your spend or your 

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stockholding. 
One of the things that stops 

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buyers from forecasting or 
sharing their assumptions is the

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belief that you need more data 
or better data. 

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But in most cases, that's not 
true. 

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You usually have more than 
enough data available. 

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It's about knowing how to use 
what is there. 

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So the first source is your own 
sales history, last season's 

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performance by product type, by 
category, by price, tier, sell 

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through rates, average selling 
prices, markdown depth. 

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All of this data tells you what 
your customer actually bought 

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when given the choice, not what 
you hoped they would buy. 

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It is the most direct signal 
that you have. 

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So for every new buying decision
you make, you will generally 

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look to how a comparable product
sold and how did it perform. 

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And the second source is your 
market context. 

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What is happening in the 
competitive landscape? 

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What are comparable retailers 
doing with similar product? 

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What price points are selling 
and what are stalling? 

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This does not need to be formal 
research, it's the ongoing 

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market awareness that good 
buyers develop as a habit. 

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But when it is used alongside 
sales data it adds an extra 

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dimension than just pure 
historical analysis. 

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History can tell you what 
happened, but market context 

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tells you whether the conditions
that produce that result are 

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likely to repeat. 
The third source is your in 

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season trading data. 
Once a season is running, you 

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have live information about how 
product is performing relative 

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to plan, which styles are 
selling faster than you 

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forecasted and which are slower,
where your size ratios may be 

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off, or which price points your 
customer is gravitating towards 

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or away from. 
In season data is really 

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valuable because it allows you 
to adjust as I mentioned 

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earlier, to chase into strong 
performance if your OTB allows 

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it, or to take early action on 
slow movers before markdown 

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pressure bills. 
Buyers who only look at data in 

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the context of a post seasonal 
review or a analysis are missing

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half the picture. 
It's important to review 

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historical sales data, but it is
equally important to re forecast

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and relook at your in season 
data. 

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Using all of these sources 
together. 

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Historical performance, market 
context and in season trading is

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what practical forecasting looks
like. 

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It's not very complicated but 
it's more of a discipline of how

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to use the information that you 
already have. 

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With AI integrating across so 
many parts of the business, we 

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are in danger of over analysing 
everything or having too much 

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information with which to make 
your buying decisions which can 

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lead to a lot of overwhelm. 
Try and keep it as simple as 

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possible, and if necessary go 
back to basics. 

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AI is an amazing tool for 
distilling large amount of 

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information, but as always, you 
need to be really specific about

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what you're asking and what 
results you're expecting from 

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it. 
I should point out that your 

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instincts are also really useful
when forecasting. 

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A good buyer's instinct comes 
from years of market exposure, 

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customer observation, and 
pattern recognition. 

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And it has, and it's really 
valuable. 

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The problem is not necessarily 
about having instincts. 

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The problem is using instinct 
only when data should be 

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leading, or using data only to 
confirm what your instinct has 

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already decided. 
Let me give you some examples 

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about instinct and data and 
where one doesn't necessarily 

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work without the other. 
The 1st is in range building, so

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a buyer can be really excited 
about a trend direction and 

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builds a significant portion of 
the range around it and her 

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instincts are right about the 
direction but without having a 

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forecasting discipline. 
The buy depth on individual 

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styles is guided by enthusiasm 
rather than demand planning. 

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This means while the range may 
perform because of the styles 

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are on trend, but three of the 
seven styles never sell through 

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and the markdown cost erodes the
margin that the trend was 

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supposed to deliver. 
Relying too much on buyer's 

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instinct without having a 
planning framework can cause 

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problems. 
Let me give you an example. 

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Say a buyer is really excited 
about a trend direction, 

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genuinely with commercial 
justification and builds a 

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significant proportion of the 
range around it. 

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The instincts are right about 
the direction, but without 

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having a plan, the buy depth on 
individual styles is mostly 

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guesswork and enthusiasm rather 
than demand planning. 

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And while the range performs 
well, three of the seven styles 

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never sell through and the 
markdown costs end up eating up 

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the margin that the trend was 
actually supposed to deliver. 

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Another situation can arise when
you're placing a repeat order. 

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Say, for example, you have a 
really strong selling style and 

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you believe that there is 
definitely more opportunity in 

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repeating this particular style.
Your instincts are often 

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correct. 
But if you don't have a 

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forecast, if you're not asking 
how much of that early sell 

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through is genuine ongoing 
demand versus an initial opening

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burst, what the rate of sale 
looks like week by week, and 

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whether the delivery window on a
repeat order is tight enough to 

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catch that selling period, The 
reorder could end up arriving 

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too late or in the wrong 
quantity. 

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And the opportunity is partially
missed because, again, not that 

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the instinct was wrong, but 
because there was no structured 

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framework or forecasting data to
support it. 

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And this is what I want you to 
take away from this episode. 

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Instinct tells you direction, 
but forecasting tells you the 

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quantity, the timing, and the 
risk. 

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You cannot have one without the 
other. 

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You need both. 
Forecasting is the practical 

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skill that makes everything else
in buying work better. 

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It's how the OTB becomes a 
planning tool rather than a 

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limited number, it's how margin 
targets can be achieved during 

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the buying process. 
And it's how you can maximise 

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those instincts within a 
structured buying framework. 

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That's all for this week. 
Until next time. 

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Thank you for listening to 
today's episode. 

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If you enjoyed it, make sure to 
like, subscribe, follow and 

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00:13:00,480 --> 00:13:02,680
share the show. 
Until next time.