In the earlier lessons,

we mentioned data a lot.

What big data and artificial intelligence need first is data.

Lots of it.

We have discussed the importance of databases and how

advanced technologies can generate new insights from the data.

But where is the data really coming from?

In this lesson, we will look at key data sources that are relevant to the marketers.

This will help you to plan

your own marketing activities holistically and with the goals of

leveraging big data ending our technologies as part

of your marketing campaigns and customer engagement.

Three types of big data that are a big deal for marketers: one, a customer data.

This is really the big data category I'm most familiar with in the marketing environment.

It may include behaviorial, attitudial and transaction matrix,

from such sources as marketing campaigns,

point of sale activities, websites, customer service,

social media, online communities, and loyalty programs.

The second one is operational data.

This big data category typically includes objective metrics that measure

the quality of marketing processes relating to marketing operations,

resource allocation, asset management,

budgetary controls, and so on.

And the third are the financial data sources.

Typically, those are housed in organizational financial system.

This big data categorically, mainly conceals revenue, profits,

and other objective data types that measure the financial health

of the organization and the customer engagement.

Where is the data coming from?

Big data often boil down to three main varieties.

The first one is transactional data.

This includes data from invoices,

payments, orders, storage records, and delivery records.

The second would be machine data.

This can be data gathered from industrial equipment.

For example, today's generation of aircraft could use

several terabytes of data in a single transatlantic flight.

Real-time data from sensors including sensors

on your smartphone or your heart beat monitor,

not to mention all the four million closed circuit TV cameras

around the United Kingdom alone.

And then, rep lods of track users behaviors online.

And the third one is social data.

This could be data coming from social media services such as Facebook likes,

tweets, and YouTube views.

In many cases, this data on its own is meaningless.

Real business value often comes from combining these big data feeds and

sources with the traditional relational data such as customer records,

sales location data, and really trying to figure out to generate new insights,

decisions, and actions from all of those.

Big data within a company is often coming from things like CRM system,

social media, fake usage,

IAT location services shopping data,

I'm sure you going come up with a lot of others in your environment.

The challenges related to

the effective use of big data can be especially daunting for marketing.

That's because most analytics systems are not aligned to

the marketing organization's data, processes, and decisions.

For marketing, three of the biggest challenges are knowing what data to gather.

Because data is everywhere.

You have numerous volumes of customer operation financial data to contend with,

but more is not necessarily better,

it has to be the right data.

Second is knowing which analytical tools to use.

As the volume of big data grows,

the time available for making decisions and acting on them is shrinking.

Analytical tools can help you aggregate and analyze data as well as allocate

relevant insights and decisions appropriately through out your organizations.

And third is knowing how to go from data to insight and impact.

Once you have the data,

how do you turn it into an insight?

How do you use it to make a positive impact on your marketing programs.

Well, see if some of those options.

Because it really is about bringing your team together.

To tackle these challenges,

many successful teams leverage to diverse skills and expertise within an organization.

You start with defining the goals and then the strategy or

key of a collaborative effort that drives in the same direction.

You bring together experts from marketing, sales,

online engagement, the IT department, financial teams,

that often work together to develop and find the guidance,

and that is helpful for their big data projects and insights generation.

Often, they bring in a business architect or a data scientist to

make sure that coordination is happening and the right questions are asked.

There are really three steps we're recommending to going from

big data to better marketing.

First, is use the big data to dig deeper for deep insights.

Big data affords you the opportunity to dig deeper and deeper into the data,

but peeling back layers to layers and reveal richer insights.

The insights you gain

from initial analysis can

be explored further with richer deeper insights that are emerging each time.

Second is get the insights from big data to those who can use it. There's no debating it.

The CMO's need the meaning for insights that big data can provide,

but so do Frontline store managers,

and call center phone staff,

and sales associates, and so on and so on and so on.

So, really what is good insight if it stays within the boardroom?

Get it into the hands of those who can act on it,

and make sure you're precise.

Don't try to save the world or at least not at the first step.

Taking on big data can at times seem overwhelming.

So start out by focusing on a few key objectives that are relevant for your organization.

What outcomes would you like to improve?

Once you decide, you can identify

what data you would need to support the related analysis,

and when you complete that exercise,

move on to your next objective, and your next.

Delivering customer value with analyzing transactions data can make all the difference.

Netflix built their business on customer data.

It's one of the most well known examples of big data marketing.

Netflix started in 1997 as a mail order DVD service in the US.

It was an upstart company against video store rental companies like Blockbuster Video.

There's many stories out that they can read it, but it short,

the website of Netflix features a sophisticated recommendations ensage ,

an algorithm-based program that predates

the customer's video preferences based on their past choices and

many other bits of customer data that is available from the subscribers of Netflix.

This made a tremendous value for

their users being suggestive of what's relevant for them.

So beyond being able to undercount

their Blockbuster's pricing in multiple movies rentals,

they were actually able to deliver

a customer experience far beyond what they have experienced before.

When Netflix moved from DVD rental to streaming video,

that algorithm's surfed Netflix extremely well,

and allowed them to grow their business and their customers engagement much, much faster,

both from serving the content and also from developing their own proprietary content,

because they knew what their customers want.

Data drives business success.

There's really very clear return on investment for big data.

Let's look at a couple of those.

American Express, they started looking

for indicators that could really predict loyalty develops

sophisticated predictive modeling to analyse

historical transactions and a 115 variables to forecast potential return.

The company believes they can identify

21st pro percent of accounts that will close within the next four months,

and start to strike preventative actions.

The IRS, the US Federal Tax Authority,

uses big data to stop identity theft, fraud,

and improper payments, such to those who are not paying taxes but should be.

The system really helps to ensure compliance with tax laws.

So far, the IRS has stopped billions of dollars in fraud specifically,

with identity theft, and recovered more than 2 billion over the last three years alone.

And also in retail.

The supermarket chain Kroger,

accesses customer and management data for about 770 million customers.

They're claiming 95% of sales are run up on the loyalty card.

Kroger sees an impact on these award winning loyalty programs

for nearly 60 percent redemption rates,

and over 12 billion incremental revenue by using big data and analytics.

In the hospitality, the hotel chain Red Roof Inn,

produces a 10% growth year over

year helping people who were stranded due to bad weather. How did they do that?

Combining historical weather information

and looking at the plan to target stranded airport passengers,

they had an opportunity to go after a huge amount,

90,000 stranded customers that were stranded.

The companies used a big data to

identify the areas after month and use the search advertisement,

focus on mobile communication and other messages to drive

digital booking with personalized messages like stranded out of air?

Check out Red Roof Inn.

Other industries that are using big data of course, are the telco industry.

With a huge amount of data from all kinds of transactions,

the analytics capabilities are fantastic.

Logistics?

Of course, UPS on a daily base,

they make 16.9 packages and document and devices,

and over 4 billion items shipped per year through almost a hundred thousand vehicles.

With this volume, there's numerous ways UPS uses big data,

and one of the applications is for fleet optimization.

Since starting the program,

the company estimates that over 39 million gallons of fuel have been at

safe and avoided a vehicle driving 364 million miles.

It really goes very,

very much across the board.

We have explored how big data and

AI technology have strong impact in customer engagement,

and what is really happening is the view board around us.

With this foundation, you're ready to shape

the future of your organization and your environment.

It is crystal clear that this introduction of artificial intelligence in

combination with big data will revolutionize customer engagement as we know it today.

The experience for and with our customers will change.

Many task performed by humans,

will soon be done and more efficiently at much larger scale by artificial intelligence.

The not so secret sources of all that data you should know

New Technologies for Business Leaders

Conheça os instrutores