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Marketers live in a world that is creating 2.5 exabytes of data each and every day.

This provides both a challenge and an opportunity to marketers. How can they process and harness big data in faster and more innovative ways to deliver deeper insights and improved business performance?

While big data has suffered from its fair share of hype, it is fundamentally changing the marketing landscape.

However in a world with so much data, marketers don’t necessarily know where to begin. Based on my experience, here are the top four areas where big data benefits marketers.

Data is also one of the central themes at the Econsultancy Festival of Marketing event in November.

It's a two day celebration of the modern marketing industry, featuring speakers from brands including LEGO, Tesco, Barclays, FT.com and more.

1. Improved forecasting and planning

Marketers have always relied on forecasting to understand how individual marketing tactics deliver success. Big data enables marketers to increase the volume and range of information sources while speeding up reporting, enabling real-time forecasting and more informed decision-making. 

This means marketers can see how returns can be increased with extra budget, understand how different levels of spend affect revenue, and ascertain the potential of particular campaigns. 

However there are challenges to big data-driven forecasting. Outside factors, such as market conditions and consumer demand, are ever-changing.

Your own stock and pricing models are dynamic, meaning forecasts need to evolve quickly. Finally a lack of sufficient data can potentially derail your forecasts. 

The good news is that predictive modelling can overcome these issues. By using powerful algorithms that anticipate and react to changing conditions or models that cluster data points together based on similarity, marketers can improve forecasting accuracy across their operations.

2. More granular audience targeting

Marketers benefit from a huge amount of targeting options when it comes to online advertising. The growth of cookies and information-rich social media, means that the data is there to go beyond simple demographic, geographic, and time-based targeting options. 

Being able to tap into this information at scale through big data programmes unlocks the ability for much more intelligent targeting.

However there are three key challenges that need to be overcome:

  1. Consumer concerns on privacy.
  2. The difficulty of mobile and cross-device tracking.
  3. Integrating cross-channel and offline tracking.

These mean that advertisers need to be transparent and compliant to local laws to reassure consumers, while in areas where targeting is not yet fully developed or integrated, rely on the signals they do have to make informed decisions.

At the same time a lot of companies are looking at ways of measuring cross-channel behaviour, making it easier in the future to integrate campaigns across offline and online. 

3. Optimising campaigns in real-time

Understanding current performance enables marketers to optimise their campaigns and improve results in real-time.

While it is potentially possible for humans to look at performance data, analyse it and make optimisation decisions, big data adds a huge number of additional variables that are difficult for our brains to compute. 

Big data technology, such as algorithmic decisioning processes, can provide the sheer processing power needed to optimise campaigns in a scalable, real-time manner.

For example it can be used to determine the proper bid for each keyword in a portfolio so that they are all working together toward one shared goal. 

In recent research by Econsultancy nearly half of all marketers said that data-driven optimisation is one area where they are ahead of the curve.

Areas where client-side marketers believe their organisation is ahead of the curve

The same survey found that 16% of marketers felt that they were behind the times when it came to optimisation.

There are a couple of reasons for this. Smaller companies may worry that they haven’t got sufficient data or the skills needed to optimise, while there can be pushback against this completely new way of working.

To move beyond these fears, marketers should look at the different technologies in the market and educate themselves on how similar companies have benefited.

4. Moving to multi-touch attribution

From the first web adverts, marketers have predominantly used the last-click model for conversion attribution, even though multiple factors tend to impact conversions.

The problem was that previously capturing these different, interrelated factors and understanding their relative value was difficult. Big data changes this, by making it easier to track, analyse and evaluate activity across the consumer purchase life cycle.

Consequently marketers can adopt a multi-touch approach which takes into account all touch-points.

Marketers can use these multi-touch attribution (MTA) models to get a better insight into campaign performance data and the see what is actually driving consumers to engage.

While MTA models deliver deeper understanding of the factors impacting conversions, nearly half of all US marketers still measure their efforts through last-touch metrics, with less than 5% using algorithmic models.

Primary method used to measure marketing effectiveness

There are both technical and operational reasons for this:

Technical Challenges

  1. Deletion/blocking of cookies.
  2. Difficulty of tracking across multiple channels and devices.
  3. Difficulty of integrating offline data.

Operational Challenges

  1. Market complexity – lots of partners, opinions, and options.
  2. Lack of adoption with stakeholders and silo-based working.
  3. Inability to acting on insights.

The technical challenges can be overcome, with multiple options available to support your campaigns. What can be more difficult are the operational challenges as they revolve around cultural and organisational factors, such as a silo-based approach to marketing, with different teams essentially competing for conversions (and consequently budgets).

The best way forward is to align your teams around the same incentives – e.g. overall revenue, ROI, CPA – rather than channel-specific goals.

This cuts down the in-fighting and enables a holistic, whole company approach to measuring and sharing success.

Big data promises to transform marketing as a discipline, providing the ability to better understand customers, in real-time, on a vast scale.

Like any new technology that changes the status quo marketers need to understand it in-depth before beginning to implement it.

Matthew Whitehead

Published 5 August, 2014 by Matthew Whitehead

Matthew Whitehead is Lead Solutions Consultant, EMEA at Kenshoo UK Ltd  and a contributor to Econsultancy.

15 more posts from this author

Comments (1)

Pete Austin

Pete Austin, CINO at Fresh Relevance

I struggled with this post, because those are not the applications of Big Data that I'd have chosen. Then I realized that it focuses on non-customer-facing applications of Big Data: forecasting, targeting, targeting again and attribution.*

All valuable uses, but it's not the whole picture.

I see much greater gains from "real-time big data" in the real-time/customer facing space.

Examples include:
1. Triggered messages such as cart abandonment which have massive ROI,
2. Hyper-personalization such as product suggestions,
3. Open-time email content giving you the same real-time content control that you have on web pages.
4. Real-time data integration for consistent omni-channel marketing.

* [Targeting is non-customer-facing because it's like picking from a limited menu of predefined marketing messages - not using big data to generate the customer-facing message].

over 2 years ago

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