The importance of analytics during economic ambiguity

Brexit caused a whirlwind of economic confusion that shocked many when the results of the referendum were revealed on that warm summer’s morning of June 23rd 2016. It caused a shift in the British economy that solidified its uncertainty, and still we do not know what the outcome of the results will mean in the future. However, what we do know is that businesses are likely to suffer, which is why the need for analytics is crucial during a time of such ambiguity.

Consumers are spending less, because they have less

The GPB has devalued significantly leading to an increase in import costs and ultimately, an increase in price of consumer goods.

Chart: GBP vs the USD/EUR following Brexit

Inflation fired up to 3% this October, which is the highest it has been since 2012, unfortunately earnings only grew 2%. With the price on goods increasing and wages stagnating, living standards are suffering and this typically means consumers will get more price sensitive.

Chart: UK Inflation at its highest level for years

This particularly affects the FMCG and retail industry because people have less disposable income. High street retailers are also facing tough competition from online retailers such as Asos. Next and New Look were one of many fashion chains that were struck hard this year with a loss of £10.4m YTD Sep ‘17 compared to a profit of £59.3m during the same time frame of last year. This is likely to have alarm bells ringing for many brands with only six weeks to go before Christmas.

There is also the issue of the increasing rise of fuel and raw materials. In a bulletin from the Office of National Statistics, living costs have risen by 8.6% in comparison to 7.6% last year due to an increase in the price of food and airfares. Brands will have  to minimise the effects of rising prices by increasing prices where they are likely to lose less sales and support key price points on important footfall drivers.

Price elasticities provide rescuing results  

So how do we prevent businesses from taking heavy falls in the market? By measuring price elasticity in detail and on a frequent basis, you will know which product lines to increase price on (where the elasticity is lower), and which products to leave alone (where the elasticity is higher). This ensures that there is a minimal impact to the business in terms of lost sales, and can lead to increases in revenue and profit – despite the gloomy macro-economic situation.

At Brightblue we have launched a new automated predictive modelling product that can track and report price elasticity on a monthly basis. This can produce real time price optimisation results, which will ultimately save businesses significant amounts of money.

Clearly in unstable economic times there is even more need to understand what works and what doesn’t. By using data and sophisticated analytics, retailers are able to leverage this competitive advantage to help minimise losses and rise above the competition.

Mike Cross

When is evidence of marketing uplift not evidence?


These days we can access data at any time: clicks, website visits, likes, shares, search, live sales by minute, by second… but how do we make sense of this data? Why are uplifts in these metrics not the same as campaign success? As a marketer you must have heard the old adage that correlation does not mean causation. But what are the risks and implications of making big decisions based on these types of data and simple correlation?

When data moves in the same way it does not necessarily mean they are related to each other. Spurious correlations can be detrimental if not spotted – or if you like, is indeed spotted, but not recognised as such. Specifically the case where two metrics move in the same direction, but not because they are interacting, but rather because they are both caused by a third underlying factor or just happen to occur at the same time. For example, as ice creams sales increase the rate of drowning also increases which therefore must mean that the consumption of ice cream causes drowning – in fact, the true driver (the latent factor) is summer seasonality.

So how can we make sure that these faulty correlations are recognised for what they are?  Well, when assessing the success of activity there are a few factors we must consider like for example seasonality. What types of activities fuel other activities such as promotions, world events, social media, mail order and other forms of advertising? Are there activities promoting new product line launches or perhaps expanding on an existing product like creating a new flavour perhaps?

What are the results of misinterpreting data? Misallocating resources is a real possibility by using media platforms that simply do not work. Back to our swimming example: imagine trying to stop drownings by outlawing the sales of ice cream!

This is why econometrics is so powerful and at BrightBlue we take on a holistic approach to modeling because we know the pitfalls.

Ruan van de Venter

Brightblue launches Dynamic Attribution

One of the current conundrums marketers face is attributing budget across offline and online channels in a fair and accurate way.  Currently this allocation is completed in silos; Pathway Attribution, focussing on detailed digital attribution, and Market Mix Modelling, which focusses on broad channel attribution.

Pathway attribution uses cookie level data to understand how each online channel has contributed to a sale, therefore giving the ability to source very detailed ROIs but for Online Channels only.

 

Market Mix Modelling (MMM) uses data over time to build models of sales / KPI drivers and isolate media impacts, delivering broad (non-detailed) ROIs for offline and online channels (TV, Radio, Press, Display, PPC, etc).

 

The limitation for attribution modelling is not considering offline factors, whereas market mix modelling struggles to understand intra-online channel ROI.

 

Recent ‘best practice’ in this area has focussed around merging the two techniques via ‘Total Attribution’ or ‘Re-attribution’, which simply uses the results of MMM to scale the detailed attribution results in a 2-step process.

 

But shouldn’t there be an interaction between the two types of analysis?  It’s common knowledge that digital and offline channels do not act in silos – for example a TV ad will push consumers online to look for a brand, they then search and click on a PPC link – thereby the TV would inflate the PPC response.  So surely the different methods of measurement should be able to adjust to this relationship?

That is why Brightblue is launching a pioneering product, Dynamic Attribution, to join both pathway attribution and marketing mix modelling (MMM) into one analytics platform. Dynamic Attribution delivers the true quantification and measurement of channels, platform and creative across online and offline media.

Brightblue’s new solution dynamically uses machine learning techniques to bring together MMM and attribution modelling to deliver more accurate and detailed media ROI. Dynamic Attribution delivers huge potential for detailed optimisation opportunities across all offline and online media channels.

 

Please get in touch with Amy Pritchard if you’d like to find out how we can dynamically evaluate your media channels in detail.

Brightblue launch real-time MMM analytics

The modern marketer operates in a much faster environment than of old; this, coupled with mountains of streaming data, can make it difficult to make quick, informed decisions – especially around campaign performance.

Traditional techniques to quantify campaign performance, such as market mix modelling, take time to source, manipulate, analyse and report on new data streams; usually a 2 month lag between campaign end and results.

To solve this problem we have launched a new analytics platform: Realtime Analytics, which helps to steer brands and businesses in these fast changing, fluid markets.

Realtime Analytics facilitates quick decisions and adapts plans and investments using the latest knowledge of performance on a month to month or week to week basis.

 

Our approach uses APIs to draw data into the cloud and combines advanced market mix modelling with machine learning techniques to dynamically update models.  This enables brands to monitor their drivers of growth and progression of ROI on a monthly (or even weekly) basis.

 

This helps our clients get an almost instant read on campaign performance, allowing them to use hard analytics to react faster and refine plans to achieve maximum growth in the modern, fast-paced world.

 

Please contact Amy Pritchard if you would like to see how we can help accelerate your business.

 

Out of the Blue – July 2017

 

Out of the Blue July ’17

Interesting snippets from the world of data

For my birthday edition of Out of the Blue I’ve gone for some of my favourites: websites, cars, heavy metal bands and some cool charts (great!) on Brexit (not to so great).

 

Top 100 websites 
The majority of these websites belong to only 12 holding groups (e.g. Alphabet, Amazon, Facebook, etc).  At least pornhub is doing ok at nb 33…
 

Diesel/Petrol car ban cost
Its great the UK is going greener, but at what cost?  The trend shows diesel/petrol cars being obsolete by 2045 & the strain and cost on the national grid.
 

Metallica in numbers 
Cool infographic on Metallica’s concerts – which songs, where and what the audience would like to hear.
 

The best cheat sheet for Brexit, ever!
All you need to know in one handy place

We’ll also be announcing some exciting stuff over the coming weeks on:
– Real time modelling
– Dynamic attribution across online and offline
– Modelling partnership with FacebookIn the meantime, hope you had a great July and enjoy the rest of the summer!Til next time…Mike

 

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