Traditional methods of sales forecast undergoing disruption
Apple iPhones have just had their most terrible quarter in India since over their decade long existence here (despite few new product launches) and Apple itself forecasted its first ever fall in revenues.
- Apr 4, 2016,
- Updated Apr 4, 2016 4:23 PM IST
Apple iPhones have just had their most terrible quarter in India since over their decade long existence here (despite few new product launches) and Apple itself forecasted its first ever fall in revenues. The same quarter was Apple's best-ever overall with iPhone sales climbing 76 per cent in the country from the year-ago quarter. The contrasts illustrate how sales forecasts in a company product or segment-wise is complex and even very big companies find it difficult and extremely challenging to steer away from market uncertainties.
Even if smarter companies have designs and innovations to build blue ocean products and services, they necessarily find it incapable to sustain growth q-o-q. Also, customer segmentation may help forecast one part of the problem (a piece in a pie) but it may fail to take into account the entire market size; the pie is forever changing. It seems sales forecast has to have a faster stimulus towards changing consumer psyche, demographics and preferences.
While there are blue ocean strategies clearly defined by companies, consumer fatigue is far easier to set in than it was years ago. Changing demographics, affordability, multiple choice, hyperlocal sellers, bigger competition et al are making things more multifaceted for both marketers and sales analysts to contain. Black Friday, the shopping concept that was established in Roosevelt's time in 1939, was a huge success in the US in its initial years but it started showing signs of gray as for consumers it became less of a temptation, retailers started offering the same bargain prices in the days before and after the big shopping event. The retailers could not hold themselves back and wait for sales to happen; hence they started offering discounts much earlier and continued to offer them even after the Thanksgiving week. Soon, it ceased to stay effective due to continued discounts to customers round the year. It started becoming more irrelevant every year and presumably now remains more as a cultural thing rather than a sales hack for retailers.
Similarly, back home here in India, sales during Diwali are also seeing a downward trend. Most of the brands have to prepare huge inventory, offer big schemes to trade partners and after the season is through, huge inventories are piled at warehouses of trade partners. This is dissuading distribution further to retail segments.
Forecast accuracy in sales is very crucial especially for seasonal or alternate season products like air-conditioners or refrigerators. Earlier, around Durga Puja, east of India used to witness an upsurge in purchase of consumer durables; now it has shifted more towards textiles and household equipment. The trend for seasonal products will only grow more unpredictable and tedious for sales analysts.
Consumers are dissociating themselves from a product in a way that a product fails to grow fond experiences and connect with them; this can be alarming for sales and may contribute in forecasts of sliding revenues. In last quarter of 2014, McDonalds faced de-growth with 30 per cent decline in profits. McDonald's Corp Chief Executive Officer Don Thompson accepted that customers see the company little more than a manufacturing plant (and presumably less than a restaurant) underlining issues with imagery affecting sales, clearly indicating no quick solution to that.
Walmart, an American multinational retail corporation, has some 11,528 stores in 27 countries but failed in Brazil, South Korea, Germany and China. In India, it operates in over 21 locations under the label 'Best Price' but is seen stumbling here too for various reasons including violating FDI rules. In Germany, it is believed Walmart could never establish cordial relations with labour unions and European culture seemed different to what one experiences in the US.
Walmart followed rival Carrefour in exiting South Korea; it sold all 16 of its outlets to Shinsegae, a local retailer, for $882 million. Analysts believed it failed to localise its operations and adjust to consumer tastes, hence could not press suppliers on pricing. It acquired its 'multinational' status sometime in the late 1990s; till then it was growing stronger as a regional player. Its expansion to other countries was perhaps too rapid and it could not forecast some things that were possible only after entering and operating in a territory.
Aren't there times when most of us become impulsive buyers of things we actually don't need, but they land in our shopping carts and into our homes without much effort? Similarly, companies at times find it immensely difficult to cope and differentiate between irrational and logical buying patterns of their customers. The question still remains - how can global companies customise and segment customers at a very micro level without compromising on sales and profitability? Perhaps, keeping sales targets practical is one way of looking at it, which is easier said than done. Needless to state, there is more science required to practise this art skilfully.
A US-based weather channel, weather.com, uses data to help companies sell products based on weather; this is other than the supposed primary task of just forecasting weather. Consumers' relationships with the products that they use change with every change in a season; companies would like to know what and how consumers are influenced and are ready to use their products according to the weather that day. The company has collaborated with Walmart Stores and P&G to gather sales data from all the stores across US.
They correlate sales in various stores as per the weather. Their team matched the information with the past 30 years of local weather data and revealed surprising and specific sales trends with their data science. For e.g. they can tell in Miami, if a set of weather conditions occur, people will buy a certain brand of raspberry. P&G's Pantene learned that people didn't actually buy products to control their humidity-frizzed hair on a humid day. They needed some time before they could shop. Pantene ran location-specific ads on the Weather Channel's mobile app, offering free 'haircasts' that would tell people how flat or frizzy they could expect to look during the next few days. Pantene's sale of advertised products jumped 28 per cent with that.
Data science, like many other industries and segments, is the new hope even for sales forecasters. Beyond season and weather, companies especially e-commerce or internet companies are now also able to figure out consumer psyche leading to new purchases or replacement purchases. In economic crisis or recession, accurate or closer to truth sales forecasts is the need of the times.
The author is a veteran in consumer durables and retail, and is currently consulting with World Bank
Apple iPhones have just had their most terrible quarter in India since over their decade long existence here (despite few new product launches) and Apple itself forecasted its first ever fall in revenues. The same quarter was Apple's best-ever overall with iPhone sales climbing 76 per cent in the country from the year-ago quarter. The contrasts illustrate how sales forecasts in a company product or segment-wise is complex and even very big companies find it difficult and extremely challenging to steer away from market uncertainties.
Even if smarter companies have designs and innovations to build blue ocean products and services, they necessarily find it incapable to sustain growth q-o-q. Also, customer segmentation may help forecast one part of the problem (a piece in a pie) but it may fail to take into account the entire market size; the pie is forever changing. It seems sales forecast has to have a faster stimulus towards changing consumer psyche, demographics and preferences.
While there are blue ocean strategies clearly defined by companies, consumer fatigue is far easier to set in than it was years ago. Changing demographics, affordability, multiple choice, hyperlocal sellers, bigger competition et al are making things more multifaceted for both marketers and sales analysts to contain. Black Friday, the shopping concept that was established in Roosevelt's time in 1939, was a huge success in the US in its initial years but it started showing signs of gray as for consumers it became less of a temptation, retailers started offering the same bargain prices in the days before and after the big shopping event. The retailers could not hold themselves back and wait for sales to happen; hence they started offering discounts much earlier and continued to offer them even after the Thanksgiving week. Soon, it ceased to stay effective due to continued discounts to customers round the year. It started becoming more irrelevant every year and presumably now remains more as a cultural thing rather than a sales hack for retailers.
Similarly, back home here in India, sales during Diwali are also seeing a downward trend. Most of the brands have to prepare huge inventory, offer big schemes to trade partners and after the season is through, huge inventories are piled at warehouses of trade partners. This is dissuading distribution further to retail segments.
Forecast accuracy in sales is very crucial especially for seasonal or alternate season products like air-conditioners or refrigerators. Earlier, around Durga Puja, east of India used to witness an upsurge in purchase of consumer durables; now it has shifted more towards textiles and household equipment. The trend for seasonal products will only grow more unpredictable and tedious for sales analysts.
Consumers are dissociating themselves from a product in a way that a product fails to grow fond experiences and connect with them; this can be alarming for sales and may contribute in forecasts of sliding revenues. In last quarter of 2014, McDonalds faced de-growth with 30 per cent decline in profits. McDonald's Corp Chief Executive Officer Don Thompson accepted that customers see the company little more than a manufacturing plant (and presumably less than a restaurant) underlining issues with imagery affecting sales, clearly indicating no quick solution to that.
Walmart, an American multinational retail corporation, has some 11,528 stores in 27 countries but failed in Brazil, South Korea, Germany and China. In India, it operates in over 21 locations under the label 'Best Price' but is seen stumbling here too for various reasons including violating FDI rules. In Germany, it is believed Walmart could never establish cordial relations with labour unions and European culture seemed different to what one experiences in the US.
Walmart followed rival Carrefour in exiting South Korea; it sold all 16 of its outlets to Shinsegae, a local retailer, for $882 million. Analysts believed it failed to localise its operations and adjust to consumer tastes, hence could not press suppliers on pricing. It acquired its 'multinational' status sometime in the late 1990s; till then it was growing stronger as a regional player. Its expansion to other countries was perhaps too rapid and it could not forecast some things that were possible only after entering and operating in a territory.
Aren't there times when most of us become impulsive buyers of things we actually don't need, but they land in our shopping carts and into our homes without much effort? Similarly, companies at times find it immensely difficult to cope and differentiate between irrational and logical buying patterns of their customers. The question still remains - how can global companies customise and segment customers at a very micro level without compromising on sales and profitability? Perhaps, keeping sales targets practical is one way of looking at it, which is easier said than done. Needless to state, there is more science required to practise this art skilfully.
A US-based weather channel, weather.com, uses data to help companies sell products based on weather; this is other than the supposed primary task of just forecasting weather. Consumers' relationships with the products that they use change with every change in a season; companies would like to know what and how consumers are influenced and are ready to use their products according to the weather that day. The company has collaborated with Walmart Stores and P&G to gather sales data from all the stores across US.
They correlate sales in various stores as per the weather. Their team matched the information with the past 30 years of local weather data and revealed surprising and specific sales trends with their data science. For e.g. they can tell in Miami, if a set of weather conditions occur, people will buy a certain brand of raspberry. P&G's Pantene learned that people didn't actually buy products to control their humidity-frizzed hair on a humid day. They needed some time before they could shop. Pantene ran location-specific ads on the Weather Channel's mobile app, offering free 'haircasts' that would tell people how flat or frizzy they could expect to look during the next few days. Pantene's sale of advertised products jumped 28 per cent with that.
Data science, like many other industries and segments, is the new hope even for sales forecasters. Beyond season and weather, companies especially e-commerce or internet companies are now also able to figure out consumer psyche leading to new purchases or replacement purchases. In economic crisis or recession, accurate or closer to truth sales forecasts is the need of the times.
The author is a veteran in consumer durables and retail, and is currently consulting with World Bank