demand forecasting

9月 272017

Depending on who you speak with you will get varying definitions and opinions regarding demand sensing and shaping from sensing short-range replenishment based on sales orders to manual blending of point-of-sales (POS) data and shipments.        Most companies think that they are sensing demand when in fact they are [...]

Is demand sensing and shaping a key component of your company’s digital supply chain transformation? was published on SAS Voices by Charlie Chase

6月 272017

Let me start by posing a question: "Are you forecasting at the edge to anticipate what consumers want or need before they know it?"  Not just forecasting based on past demand behavior, but using real-time information as it is streaming in from connected devices on the Internet of Things (IoT). [...]

Forecasting at the edge for real-time demand execution was published on SAS Voices by Charlie Chase

3月 152017

Omnichannel Analytics are helping companies uncover patterns in big data to improve the customer experience.  Using those insights, companies can anticipate what consumers are planning to purchase and influence that purchase in real time.     Companies are experiencing unprecedented complexity as they look for growth and market opportunities. Their product portfolios are [...]

Omnichannel is changing the way we view demand planning was published on SAS Voices by Charlie Chase

3月 152017

Omnichannel Analytics are helping companies uncover patterns in big data to improve the customer experience.  Using those insights, companies can anticipate what consumers are planning to purchase and influence that purchase in real time.     Companies are experiencing unprecedented complexity as they look for growth and market opportunities. Their product portfolios are [...]

Omnichannel is changing the way we view demand planning was published on SAS Voices by Charlie Chase

12月 202016

Black Friday 2016 took everybody by surprise. The biggest shopping day of the year is a crucial date in any retailer’s calendar. And rightly so. Auditors and analysts predicted that 2016 would see the majority of consumers splashing out more cash than ever before on everything from scented candles to […]

5 ways for retailers to thrive in post-Brexit Britain was published on SAS Voices.

10月 072016

Machine learning is taking a significant role in many big data initiatives today. Large retailers and consumer packaged goods (CPG) companies are using machine learning combined with predictive analytics to help them enhance consumer engagement and create more accurate demand forecasts as they expand into new sales channels like the […]

Machine learning changes the way we forecast in retail and CPG was published on SAS Voices.

2月 152016

Preparing for Christmas now may seem extreme to the everyday shopper – but that’s exactly what retailers are doing. Getting a head start will take the pain out of the busiest retail period of the year and deliver a degree of predictability in an otherwise unpredictable year. In the heat […]

Retailers: Christmas planning starts today! was published on SAS Voices.

11月 162015

Once upon a time, the festive countdown began when towns and cities switched on their Christmas lights. We all sat at home, eagerly waiting for Christmas adverts to debut. Retailers could plan based on the number of weekends left until Christmas. But last year, all that changed – the Black Friday phenomenon arrived in the UK and totally changed the way we shop for Christmas.

This year, Black Friday week is expected to be more popular than the week before Christmas for festive shopping. One in five British shoppers plan to go bargain hunting, with 25- to 29-years-old being the most likely group to shop during that week according to our research. Younger age groups are notoriously difficult to predict as they are the most likely to compare prices online and are more open to purchases via different channels, incRetail Offersluding mobile. It’s putting retailers on the spot as so much of their revenue will be determined by a single day’s trading. Amazon and Argos have even started their Black Friday sales three weeks early to bring some order to proceedings!

But, retailers be warned. Brits may love a good price - three in four (75 per cent) of us are primarily motivated by price and half of us (51 per cent) are motivated by getting a bargain. But just under half of us (46 per cent) by the product being in stock. Even if retailers tick all the boxes to win over consumers, long queues are one sure way to lose sales. Consumer tolerance for bargain hunting is limited to one-minute for each one per cent discount when waiting for a store to open. This ‘patience ratio’ drops to about 40 seconds for each per cent discount at the checkout.

It all means retailers are facing the most unpredictable Christmas shopping period yet. Black Friday has changed market dynamics from a fulfilment and a predictability perspective, and for many retailers it’s set to be the busiest trading day this year. Price wars are extremely difficult to forecast and cater for when squeezed into a shorter timeframe. If retailers don’t attract enough customers they lose out, but if they can’t deliver on what they promise they also lose out.  It’s a difficult balancing act. And it doesn’t end there. The channel used must be evaluated – John Lewis announced earlier this year that it would now be charging for its ‘Click & Collect’ service as it was costing them too much to deliver it free of charge. Regional variations and weather patterns complicate the picture too.

Analytics can spare retailers a nerve-jangling finger-in-the-air experience this Christmas. By extracting insights from data in marketing, merchandising, supply chain, operations and more, it gives them the ability to make evidence-based decisions as to what is driving demand for which customers, when and via what channel based on their habits and preferences.  Only then can they make sensible decisions about pricing, stock levels and optimising their resources and supply chain.

It’s intriguing to see what happens next and who the winners and losers are.  Some US retailers, such as REI, have even pledged to remain closed on Black Friday this year – basically removing themselves from the game. One factor is that it’s a holiday period in the US, being the Friday after Thanksgiving, so there’s some feeling it should be a quiet time with friends and family rather than a time for frantic shopping.

To find out more about UK consumer spending habits this Christmas, check out the key findings from our 2015 SAS Christmas Shopper Survey.

tags: analytics, demand forecasting, online store, retail, Supply Chain

Retailers facing most unpredictable Christmas ever was published on Customer Analytics.

8月 272015

I realized a little while ago that I may have more loyalty cards and memberships than the average person. (And that I more actively prove my loyalty than the average person). But as anybody who has ever signed up to a mailing list or for a store card knows, having a loyalty card doesn’t necessarily guarantee loyalty (unless you think of shopping as a sport). It just means that at one time we were enticed by a “shiny object” or a great consultant that deserves a raise.

There are statistics, statistics and statistics out there on loyalty but here are a few that total the rest:

  • On average, loyal customers are worth up to ten times as much as their first purchase – White House Office of Consumer Affairs
  • The probability of selling to an existing customer is 60 – 70%. The probability of selling to a new prospect is 5-20% – Marketing Metrics
  • It costs six to seven times more to acquire a new customer than retain an existing one – Bain & Company

So yes, at the risk of seeming self-serving, it is worth keeping an avid shopper like myself happy.

In the first of this series I listed the things that would keep most consumers happy. I’ll cover the first two in this article. There are, of course, nuanced complexities across different markets and retail sectors (diamond rings versus ice cream versus wrapping paper), but simply put, customers want a good relationship with their retailers and service providers. If you don’t believe me, refer to the statistics.

Let’s get to the cashier at the end of the aisle – how do we use the data we have about our consumers, products and services and market to be successful? I refer to any entity that transacts as a customer e.g. individual, household, business, etc.

The right product – what product, when and for how long?

We know, even just by observation, that there are products and services that are most appropriate, or in demand, at either certain times of the year, or stage of a person’s life. For exampretailforecastle, don’t try to sell me income insurance when I am about to retire, but feel free to sell me froyo all year long.

Understanding an individual customer’s stage of life is important when targeting them directly (more on this in the next section), but to understand “the right product” to have in stock, the trick is to know how demand changes throughout time. This is called demand sensing – understanding the troughs, spikes and plateaus of demand, throughout a year and over years.

We sense demand by using time series forecasting techniques that break demand down to:

  • Trend – is demand increasing or decreasing on average?
  • Seasonality – does demand generally peak or drop at certain times of the year?
  • Known – do we have data to support a spike or trough like promotions, holidays, economics?
  • Unknown (because we can’t know everything).

The most common time series forecasting techniques are ESM and ARIMA models. ARIMAs have an added advantage over ESMs of being able to take “known” factors (events, holidays and other information that may impact demand). These types of models are sometimes referred to as causal models because they can measure the impact, duration and complex interactions between trend, seasonality and known factors. For example, sweaters need to be discounted by 80% in summer unless recently worn by Taylor Swift on an album cover released within the last 6 months and/or exclusively sold online for 2 days to countries in the other hemisphere.

These models also allow us to understand the impact of different scenarios e.g. what if we run the promotion for 3 weeks instead of 2, what if we increase prices by 10% because of supplier shortage. Applying advanced forecasting techniques like these helped Nestlé Oceania more than halve their forecast bias, streamline their process and better understand the impact of their promotions.

Once we can sense demand we can then start to shape demand by optimizing across the supply chain. For more on demand forecasting follow The Business Forecasting Deal blog or in this title.

Good service - who to target, how to target and why target?

Ok, this is a given. Well-trained, customer-centric staff is precious gold, but knowing what trained staff should offer and when to make the offer is conflict-free customer service diamond.retailcustomer

Some types of data attributes that are useful for predicting the best treatment to give a customer:

  • Transactional – purchases or inbound interactions made by the customer
  • Behavioural – responses by the customer to interactions and patterns in transactions
  • Geodemographic – statistical characteristics of a customer e.g. gender, region of residence
  • Derived 3rd party – data accumulated over various sources providing pre-calculated metrics that can be applied to a customer.

If a customer always shops when they are given an offer (SALE!), they will probably continue that behaviour. However, as we know, people don’t all behave the same way, and trying to get a headline description of our "target customer" isn't easy.

One method to generalize customers is to group them into segments using data attributes. A common technique is Recency, Frequency and Monetary/Value segmentation (RFM). RFM uses data on how recently a customer transacted, how often a customer transacted in a period of time, and how much a customer has spent/cost in a period of time to split customers up across a cross-section of those dimensions e.g. low tenured but high value customers, high value lapsing customers, etc. Keeping customers (like me) that are high R, F and M happy will generally work out well.

If we want to take it to the next step, we need to learn from attributes of customers that have and have not responded to offers and channels in the past, create a picture (model) of how to differentiate them, then extrapolate the model to give us what is likely to occur in the future. This is called propensity modelling, a form of predictive modelling. Using statistically-based techniques rather than business rules, removes the need for us to pre-suppose every detail about every customer. These techniques are used to predict the likelihood of a customer taking up an offer, lapsing from a loyalty program or even the life stage and lifetime value of a customer. For larger data, this is best carried out using machine learning techniques in a data mining framework for automation and validation.

One step further is to optimally allocate an offer amongst competing offers and channels to a customer while accounting for operational constraints (budget, resources) and customer preferences (frequency of contact, other products of interest). New Zealand’s leading coalition loyalty program Fly Buys, uses a combination of these techniques to target customers with the best offers and maximise the return their partners gain from the program.

Finally, the ability to understand what customers are saying in surveys, through the call centre and on social media forums about our products, services and processes adds further power to predictions. Applying text analytics techniques that use a combination of machine learning and linguistic rules to extract sentiment, discover discussion topics and predict outcomes is how organisations like Lenovo decreased the number of call centre requests by 30%-50%. Marrying behaviour with feedback is the fundamental objective of good customer service and the basis of concepts like Net Promoter Score.

“A feeling that I got a good deal” and “Convenience” in II.B. Until then, if you want to go beyond a “shiny object” to a meaningful relationship with your customers, start an Analytics conversation within your organisation by sharing the case studies above and visiting SAS Advanced Analytics to get more information on the boutique techniques described. For those out for a quick shop, visit SAS Visual Analytics.

tags: analytics, customer experience, demand forecasting, retail

A Shopaholic’s Guide to Analytics II.A was published on Left of the Date Line.

4月 232015

Profitable growth is at the forefront of manufacturing executives’ minds¹.  The math is simple:  increase revenue and decrease costs.  Easy, right?  Unfortunately, getting there isn't that simple.  The good news is that analytics can help.  The better news is that there’s a new place for manufacturers to discover analytic best […]

The post New source for manufacturing best practices appeared first on SAS Voices.