This is the final part of the series. Please read the earlier parts before going through this post or it won't make sense. Links below:

Working with Numbers - Part 1

Working with Numbers - Part 2

Working with Numbers - Part 3

The next thing to note is the CAGR for the past 3 years of the category. It is important to take a slightly longer time period as it helps negate the impact of recency effect of growths, new entrant into the category etc. Similarly, for the rest of the parameters it’s important to look at CAGR and immediate period growth. Looking at both a CAGR as well as immediate growths gives one a perspective of the trend of growth.

Working with Numbers - Part 1

Working with Numbers - Part 2

Working with Numbers - Part 3

__Working with Numbers – Part 4__

So till now we have covered 3 major sources
of information – internal numbers, Nielsen & HHP. We have also covered how
to tie in the internal numbers to Nielsen and how to use both of them in tandem
to arrive at a richer, deeper and more insightful analysis.

This post shall cover how to marry all 3
sources together and to work towards aligning marketing strategies and actions
to grow everywhere.

As with everything else in life – this post
shall work on the following assumptions:

- ·
Market Share is a marketers
pride – ultimately you are doing everything to make consumers buy “more” of you
__in comparison__to other brands - · Irrespective of what market share you have, the company wants turnover from you – that is where the “mullah” comes from and this is what funds your marketing activities
- · HHP is a true diagnostic of what actually happens and if you fix that you may be able to fix everything

Now let’s lay down some housekeeping rules:

- · Forget value for sometime – value is a derived figure – what sells is 1 unit of your product – the consumer buys 1 unit of your product and not Rs 100 worth of you
- · Figure out what is the volume metric which works for your category – and from now we will only talk in that – it could be kgs/tons/units/liters etc etc etc – throughout this post I am going to use kgs (I sell sabun after all)
- · Read the previous 3 posts for basics of the 3 sources that we are going to now intertwine and twist around and mess your mind with (evil laughter is in order here :d)

So now let’s start making the framework we
will look at for this exercise – you can of course make many many of these and
turn it all around if you want

Start from 1 common point – usually HHP is
a great way to start as it is a most complex and inflexible database.

- · Divide geographies in your internal data source and Nielsen according to how they appear in HHP. E.g. Punjab & Haryana will need to be clubbed. So would MP + Chhattisgarh and so on and so forth
- · Divide all volumes in the HHP volume metric – whatever it may be – dividing HHP volumes into other metric will make it very difficult when reading into measures like Avg. Consumption etc.

o
E.g. if you measure your volumes in tons
internally and in kgs in Nielsen – while HHP measures it in grams then please
convert everything to grams

- · Again, please remember secondaries are your only reality. Hence, you need to start with finding out your pick up in HHP and take it as a common factor for conversion

o
For eg if your internal sales
says in Delhi you sell 100 kgs and HHP
says in Delhi 120 kg is consumed then you need to take this factor and build
back into secondaries

__Framework__: Being the Bollywood freak that I am – I call it the love triangle. Ismein sab hai, romance, masala, tears, action and of course loads of drama. So here’s how the story unfolds:

*Note: The category contribution is Nielsen contribution. You can take any*

We will use the data in this table for the
rest of the post. A good exercise would be to probably populate this table and
then read further. But then that’s a lot
of work so take your pick :D

So the basic premise here in this analysis
is that there are states/geographies in this country which are pre disposed to
a consumption pattern for a particular category and hence they need to be seen
as such. For e.g. TN is one of the largest consumer of Glucose Drinks while MP
is the largest market for Hair Colours. So it’s important to sort the markets
according to their contribution to the category.

The next thing to note is the CAGR for the past 3 years of the category. It is important to take a slightly longer time period as it helps negate the impact of recency effect of growths, new entrant into the category etc. Similarly, for the rest of the parameters it’s important to look at CAGR and immediate period growth. Looking at both a CAGR as well as immediate growths gives one a perspective of the trend of growth.

Now let’s analyze each element of this grid
and see what can be done with them. This analysis needn’t only be done for
geographies – they can also be done for SKUs/Pack Sizes/Variants/formats/sub
brands or any other cut of your brand or category that makes sense.

__Contributions__: As a marketer one can have two approaches – ride on category dynamics and the other is to develop the category or geographies. However, these approaches also need to be looked through the lifecycle lens. For instance for highly penetrated categories it is important to take the category trends and contributions as a given and to align your brand to these dynamics. For not so penetrated categories or rather niche brands it is probably alright to confine oneself to the category construct. Now if one were to take either Nielsen or HHP cat contributions as a given then it is very important to look at how your brand stacks up against it. For e.g. if your internal contribution of a 10% category contribution state is only 5% then you know you are under pitched and need to take a look at these markets – especially if these are also high growth markets. Here growths don’t necessarily have to be in % terms it is also absolute growth. A big market will very rarely grow at 40-50% but a 10% growth in a big market will in all probability drive the category growth rather than a small market growing at 50%

__Growths__: Now that we have all 3 databases lets deconstruct the growth of our brand. So step 1 is to pen down growth in each geography by all three sources (only volume growth). For HHP you will need to break down the growth in volume by HH growth and growth in avg. consumption. States where all three growths are in the same direction its fine. And I mean direction which means that it will never be exactly the same – as long as the movement is in the same direction and in a +/-5% here and there it is in the same direction. In these states one should just continue to do what is being done. In states where all 3 are not in the same direction one would need to dig deeper and arrive at the WHYs. And here I think I am not going to detail this out as you must explore this on your own and am happy to answer questions J

__Trends__: One must use this analysis to find out trends for the category as well as your brand. For e.g. If the CAGR is high but the immediate growth are low then you know the category growth is slowing down and at some point so with the brand. Usually category trend leads the brand trend by a gap of a quarter or something – it’s a very rule of thumb but one can establish it for a particular category and brand. Similarly, if one were to assume a simple linear relationship of:

Household Consumption growth will drive
Nielsen Growth will in turn drive Secondary growth

Then it’s important to look for anomalies
in the three sources of information. For e.g. while your secondary and Nielsen
are showing healthy growth but the HHs or the HHs volumes are showing a slowing
down – it is a red alert situation. You would want to correct that trend
because at sometime that trend will catch up with offtakes and in turn with our
internal brand growths.

__Problem Childs__: This of course is (in my opinion) the ideal way to find out the problem child geography and then take targeted marketing actions. Once you plot geographies/SKUs/Variants or any other cut of your brand and category which makes sense and identify which of those is creating the issue for your brand or pulling down your growths, then you can target your marketing efforts only on those.

__Forecasting__: This framework is also a great way for forecasts. Once you have these numbers then you know what are the growth levers for the coming year and hence what should get focus and hence build in higher growths and ambitions. This has important implications in terms of what kind of monies will be needed to get the desired growths. E.g. depending on what are the purchase drivers for each geography, SKU, Variant the investments will need to be in the right mediums, channels etc. This also has strong implications on the sales and stock planning for the year forward. For e.g. you may need to push the sales team on which geographies to grow their distribution in and help them with accurate tools for the same. E.g. if it is a strong rural SKU then you may want to do activities like Mass Dealer Contact Programmes and on the other hand if it is a highly Modern Trade led SKU then you could plan on very strong consumer activation etc.

Important Safety Instructions – this
framework can only be used in tandem with the part 2 & 3 of these posts.
Only once you have arrived at the correct diagnostics of what is going right or
wrong in your analysis targets can you use this framework. In itself this is
just a snapshot and 1 stop shop for all information and almost like a ready
reckoner for your brand. If each element is not analysed individually then this
exercise will be futile.

hello sir,

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Thanks for sharing this Informative content. Well explained. Got to learn new things from your Blog on SAP SD

ReplyDeleteThanks for sharing this Informative content. Well explained. Got to learn new things from your Blog on SAP SD

ReplyDeleteGreat Work, Very useful Information!. Thank you :)

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