11月 202017

Just last week, Walmart announced that they'll be testing inventory management robots. These robots will cruise store aisles, scanning shelves to identify out-of-stock products and other issues. According Reuters, Walmart is testing these camera-equipped robots in a handful of stores, but plans to expand the test to 50 stores. We [...]

Effective retail planning requires precision and finesse was published on SAS Voices by Dan Mitchell

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

5月 182017

Are you caught up in the machine learning forecasting frenzy? Is it reality or more hype?  There's been a lot of hype about using machine learning for forecasting. And rightfully so, given the advancements in data collection, storage, and processing along with technology improvements, such as super computers and more powerful [...]

Straight talk about forecasting and machine learning was published on SAS Voices by Charlie Chase

3月 312017

The U.S. Marshals Service is the federal agency known for bringing wanted fugitives to justice. Often, the Marshals Service gets attention for these arrests, but once the publicity has died down they face a basic challenge --- where to put the individuals in their custody. The agency uses data to [...]

U.S. Marshals Service use analytics to save more than $200 million was published on SAS Voices by Steve Bennett

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.

5月 272016

"Correlation does not imply causation.” Does that bring back memories from your college statistics class? If you cringe when you hear those words, don’t worry. This phrase is still relevant today, but is now more approachable and easier to understand. Here at SAS, we use SAS® Visual Analytics to make […]

Correlations, forecasts, and making sense of it all with visualization was published on SAS Voices.

5月 142016

It was John Allen Paulos who said, “Data, data everywhere, but not a thought to think.” That rings true more than ever before. Companies are struggling with the deluge of data coming at them from multiple channels. But traditional data channels are just the beginning. Companies also are facing an […]

Data, data everywhere… was published on SAS Voices.

12月 222015

Although the title of this blog posting has all the ingredients to attract the eyes of an analyst, the content is targeted for all personalities of a digital marketing organization. Before we jump into the marketing analytic use case regarding forecasting, scenario analysis, and goal-seeking  for digital analytics, let's spend some time on the magic of stories. As Tom Davenport stated in his fantastic article titled, Telling a Story with Data:

"The essence of analytical communication is describing the problem and the story behind it, the model, the data employed, and the relationships among the variables in the analysis. When the relationships among variables are identified, the meaning of the relationships should be interpreted, stated, and presented relevant to the problem. The clearer the results presentation, the more likely that the quantitative analysis will lead to decisions and actions—which are, after all, usually the point of doing the analysis in the first place."

While creative visionaries and data scientists are both tremendous organizational assets within a team, it is the alliance between these two segments that will push marketing forward. Although aspirational, this is a difficult challenge to overcome. Let me begin by sharing a bit of my story - one that began with a four year career start in graphic design and creative marketing communications, and then taking making a leap to the quantitative side of marketing. I've seen and listened to how DIFFERENT these two segments of the marketing world are, and now as a preacher for the potential of marketing analytics, one's ability to make analysis interpretable and approachable is critical.

Google recently published a nice article titled, Staffing Your Marketing Measurement Team: Why You Need Data Storytellers, and one takeaway that I love from this piece is:

"The true value of data emerges when marketers are able to use it to tell a meaningful story. Enter the data storyteller, or marketing measurement analyst. This is the person who can push the tools, translate insights across the business, and motivate stakeholders to participate."

This quote nails the crux of the issue - if we don't take ACTION on the insights of analytics, it was nothing but a school project. Influencing decision-makers within an organization isn't easy, and if they do not understand the analysis, nothing will ever change. There are people who are good at creative marketing strategy, and there are people who are good at marketing analytics. However, there aren't many people who can toggle between the two, and serve as the translator who inspires both sides.

In my personal opinion, the recent surge in analytic technologies becoming more approachable is key. The special ingredient in that trend is visualization and analytics joining forces in ways we have never seen before. Why is this happening? Seeing and understanding data is richer than creating a collection of queries, dashboards, and workbooks. According to the infamous American mathematician John W. Tukey:

"The greatest value of a picture is when it forces us to notice what we never expected to see.”

The "ah-ha" moment. The best part of my work day!

In addition, when analytics becomes approachable, interpretable, and transparent to the entire marketing organization, the behavioral change of how we work together highlighted in this video becomes a reality:

Visual Analytics represents a new category of interactive and collaborative technology to provide a path to be curious and innovative. Marketers are imaginative, and are constantly pushing to analyze new and exciting data sources (i.e. clickstream, social, IoT wearables, etc.), which require the ability to scale to very large amounts of information. However, what is different here is the ability to perform sophisticated analysis, and produce visualizations to support data-driven storytelling.

Finally, we arrive at the digital analytic use case. The intention is to highlight my personal approach to tip-toeing that fine line of producing meaningful analysis, while narrating the marketing storyline. Here is the description of the business case, and my demonstration video.

Business Challenge:

How do I allocate digital media spend to drive more traffic to my website in a future time period?

Marketing Applications:

  1. Identify the most important acquisition channels (i.e. attribution)
  2. Simulate & optimize ad spend to acquire incremental traffic and meet business objective

Let me know what you think in the comments section below. If you enjoyed this article, be sure to check out my other work here. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

tags: data visualization, Digital Analytics, Digital Intelligence, digital marketing, Forecasting, Goal-seeking, marketing analytics, predictive analytics, Predictive Marketing, Scenario Analysis, visual analytics, visual statistics, web analytics

Forecasting, goal-seeking, and magical stories for digital analytics was published on Customer Analytics.

10月 212015

The Rule of Three is a writing principle that suggests that things that come in threes are inherently funnier, more satisfying, or more effective than other numbers of things – Wikipedia.

3 Ps of success, Blind Mice, Little Pigs, Stooges, Musketeers, The Matrix, The Lord of the Rings, rings, pairs of shoes, 3 year memberships… Everything is better in 3s – including this shopaholic series!

  1. A Shopaholic’s Guide to Analytics
  2. A Shopaholic’s Guide to Analytics II.A: Half a shopping bag of useful techniques in Analytics
  3. In this last hoorah on the topic of Retail (therapy) Analytics we’ll empty our bag of some the most useful analytics techniques for keeping our customers happy and loyal. From the customer’s perspective, these are “A feeling that I got a good deal” and “Convenience”.

Today I am referring to any entity that transacts as a customer – individual, household, business etc. and any item for sale or service as a product.

A feeling that I got a good deal – what offers, when and how often?

Whether it’s the word SALE or finding a rare collectable, we all want to feel that we had a fair transaction. But as a retailer or service provider, we need a balance between being competitive and fair to our customers and staying in business.

What is the impact of price on demand?
How long should a promotion run before it’s unprofitable?

shopaholics-guide-to-analytics-1Price does not affect demand of all products the same way – electricity versus floor cushions, burgers versus Porsches. The economics 101 method to understand this impact is price elasticity / sensitivity – the ratio of the percentage change in demand over the percentage change in price. “Elastic” products (ratio greater than 1) are sensitive to price changes.

However, price and demand change over time, sometimes seasonally but not always consistently – affected by economic and other environmental factors. In this case, time series forecasting “causal models” (described in “The right product”, II.A) can be used to model the relationship between price and demand directly. From this model, price elasticity can be calculated, or what-if scenarios can be run to measure the direct impact of price, taking other factors into account, at points in the future.

These techniques can also be used to quantify expected impacts of promotional activity, length and frequency and avoid over promotion.

Which creative is more appealing to customers?
Which product offer is more profitable?

Predictive models and optimisation techniques (described in “Good service”, II.A) can be used to best allocate competing offers or where similar offers have been given in the past. If there is no history, we need to test the effectiveness of our offers through experiments on small samples of customers and extrapolate these to what is likely in real-life. This is known as a choice experiment. To derive statistically viable decisions, we use experimental design to make sure we are capturing sufficient information across the different choices. A simplistic form of this is an A/B test.

Convenience – what is relevant and where?

If shopping was a sport, then as an elite athlete, I expect towels to be stocked in the locker room and the showers to be functioning. Basically, there’s enough going on in our lives – and often too many other competitive options – for customers to deal with difficult or restrictive processes.

Yes, I realise I sound like a brat. But as the e-tailer market grows, people continue to work longer hours and globalisation is a reality, it is even more important for retailers and service providers to make transacting easy. There are operational considerations – integrated systems, web design, accessibility, etc. – but there is also the need for detailed profiling to understand the viability of the target market.

What products are the most relevant?
What is the best store layout and window dressing?
What are the most effective channels?

Demographic – a profile of the different types or segments of customers and how they are likely to behave under various circumstances e.g. during lunch breaks, with young children, in retirement, etc. Using statistical segmentation techniques such as clustering or self-organising maps are useful for creating segments but profiling is the process of differentiating these segments and is done through slicing and dicing and visual exploration[1].shopaholics-guide-to-analytics-2

Where should we build the next store?
Where should we locate the distribution centre?

Geospatial and location – a profile of the geography and terrain overlayed with hotspots of activity e.g. industrial, commercial, residential, thoroughfares etc. and, to optimise decision making, demographics and economics. Geospatial visualisations and network maps are helpful to highlight and differentiate between these areas of interest.

BUT convenience is underpinned by how well we understand our customers’ needs.

Do we have enough of the right products?
Are we proving exceptional customer service?
Is there sufficient value and choice?

I hope that you have picked up a pair or two of comfy shoes to help you on your analytics journey. If you ever feel lost in the sale crowds, as with the sport of shopping, focus on one thing at a time – an “aha” moment you can make a reality. Set your well-articulated goals and invest in the right-fitting solution of people, process and technology for the relationship you want to have with your customers.

Learn more about how you can quickly get started with exploring your data in the cloud with this on-demand video, Insights in Seconds.

Happy shopping!

[1] SAS Enterprise Miner has the out-of-the-box ability to profile segments statistically using comparative graphs.

tags: analytics, price, retail, segmentation, visualization

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

10月 092015

This guest blog post comes from Dr. David Dickey, one of our original SAS Press authors. Hope you enjoy! In the late 1970s, shortly after SAS was founded, I was approached by Herbert Kirk and John  Brocklebank from SAS to put together a course on time series.  This was reasonably […]

The post How my SAS Press book was born appeared first on SAS Learning Post.