Controlling consumer touchpoints by designing from outside-in

January 21, 2013

Designing from outside-in has been on top of my mind these days and I’ve decided to write about it. If you are in Singapore, this is the Lean UX week! Do catch it if you can.

I’ve decided to cover something a little bit more traditional that I feel people kinda forgot. We talk a lot about user experience (UX), be it a device, website, let alone a service. But I found myself lost about what it really is. So I’m figuring out how to be able to, in baby-steps, design experience in a way that is simple yet effective.

Experience has been especially relevant in this era of free information where it is much more difficult to earn loyalty or simply presence in the minds of consumers. As such, marketing strategies has moved from a one-way communication push to a 2-way conversation. Or better still, one that gets viral.

The big question I’m posing here is; how do we, with limited resources, provide the best experience for a consumer in a way that will translate to brand equity? I think that it is as important to slip in nuances of your identity system so people would link that experience you provide uniquely to your brand. What kind of experiences actually matter uniquely to your consumers? That’s how you earn loyalty. It’s when you OWN a good experience in your customers minds. So in simple terms, today’s formula is;

Customer needs + Brand Promise = Brand experience

As a cumulative process, we have to provide many mini good experiences at all stages of the consumer decision process; that is pre-purchase, purchase and post purchase

purchasedecision

 

In the pre-purchase stage, we are looking at making a good initial impression. Looking professional is the basic requirement for one to consider your product, and this applies to all, from low-cost to premium products. What’s more important here is to create an accurate impression. A visual designer would come handy here to know exactly the kind of visual that would best elicit the right impression to readers at a particular touchpoint. Give a misleading visual + copy; A consumer gets the wrong expectation of the product; He ends up disappointed in your store. Such a bad experience would actually backfire on your credibility simply because you’ve just broken what the audience has perceived as your brand promise.

A good pre-purchase experience would land the brand in the consumers’ consideration list, making them move on to the purchase stage. The idea in the purchase stage is to give the consumer a taste of the product. Common examples include free product samples, test-drives for cars and free trials for software. The challenge here, is making sure that the consumer gets a good experience as how you controlled it to be, even if the consumer did not end up buying it. One could come out from a automobile showroom tweeting either of this two;

1 – “Decided to drop the option of car XXX, doesn’t feel quite right.”

2 – “Car XXX a little too sporty for me. Nice to drive but just not my type I guess :) “

Do you see the difference? Not only is the consumer complimenting but also branding car XXX as sporty. That would probably attract sporty people from that particular consumer’s network. The potent for network marketing is probably the reason why companies invest so much in making stores look good and that the staff are well trained to preach the brand promise. Starbucks scores in this area I would say. For sure their coffee is not the best in my list. From their friendly staff that would remember your name to the lucky 100th customer that gets a free drink ( I got it twice already!). These are simply but well-thought experiences that are within the provider’s control.

brand_touchpoint_wheel

Controlling post-purchase touchpoints entails delivering on your brand promise. Exceed the customers’ expectation beyond usage and performance. A lot of companies under leverage post purchase touchpoints. Increase your brand loyalty through say, efficient after sale services, loyalty coupons, newsletters, or anything to delight your customers. Invest in your customers for they are a strong marketing tool. This will get you not only repeat purchases but also customers endorsing and recommending your brand to others. Such brand equity is key to sustainable and profitable growth to any company.

designoutside-in

To sum everything up, I conclude with the notion of designing from outside-in, something I’m still trying to fully grasp - a design process that is user-centered and focused on brand experience. Be in control of the important consumer touchpoints and slip in nuances of your brand promise to increase brand equity.

Source;

Harmonizing your ‘Touchpoints’ by Scott Davis and Tina Longoria

The consumer decision journey – McKinsey Quarterly


SEARCHING: Grouping Syntax and Queries

September 25, 2012

BI24 supports using parentheses to group clauses to form sub queries. This can be very useful if you want to control the Boolean logic for a query.

To search for either “beef” or “chicken” and “London”, use the query:

This eliminates any confusion and makes sure you that “London” must exist and either term “chicken” or “beef” may exist.


SEARCHING: Range Searches

September 24, 2012

Range searches allow you to match documents whose field(s) values are between the lower and upper value specified by the range search. Range searches can be inclusive or exclusive of the upper and lower bounds. Sorting is done alphabetically.

Using square brackets performs inclusive range searches:

This will find documents whose “Branch Name” field has values between “Bristol” and “Chester” inclusive.

Using curly brackets performs exclusive range searches:

This will find all documents whose “description” fields have values between “Bristol” and “Chester”, but not including “Bristol” and “Chester”.


SEARCHING: Fuzzy Searches

September 24, 2012

BI24 enables you to do fuzzy searches by typing the tilde (“~”) symbol at the end of a single word term. For example to search for a term similar in spelling to “hip” use the fuzzy search hip~

This search will find terms like hop and ship.


SEARCHING: Wild Card Searches

September 24, 2012

BI24 supports single and multiple character wild card searches;

? Symbol for a single character wildcard search.

* Symbol for a multiple character wildcard search.

The single character wild card search looks to replace the wild card character with a single character in the search string. For example, if you wanted to find records for postcode sub areas ‘BS1 3EZ’ and ‘BS1 3ET’, you could enter for BS1 3E?

Multiple character wild card searches look to replace the wild card character with zero or more characters in the search string. For example, if you wanted to find records for the branches ‘Falkirk’ and ‘Falmouth’, you could enter for Fal*

The results returned are;


SEARCHING: Combining Search Terms

September 24, 2012

Combining search terms will provide you with a more focused set of results. – For example if you want to find the Chicken products in Birmingham, you could enter chicken birmingham:

Any records that contain the word ‘Chicken’ AND ‘Birmingham’ in any column or field will be returned and BI24 will highlight where a match is made in the record.

If you want to have the combined search term preference changed to find ‘Any’ terms (OR) rather than ‘All’ terms (AND), see Section 11 on Setting BI24 Preferences.


SEARCHING: Exact Match

September 24, 2012

Entering the exact name of the item you want to search for is an obvious place to start. For example if you’re looking for information about chicken products, type chicken in the search field then click the Search button:

Any records that contain the word ‘chicken’ in any column or field will be returned and BI24 will highlight where a match is made in the record.


Interesting findings by Columbia Business School and NYAMA study in 2012

August 1, 2012

Sample findings:

• 91% of senior corporate marketers believe that successful brands use customer data to drive marketing decisions

• Yet, 39% say their own company’s data is collected too infrequently or not real-time enough

• And 51% say that a lack of sharing customer data within their own organization is a barrier to effectively measuring their marketing ROI

• Large firms are much less likely to collect new forms of digital data like mobile data (19%), than they are to collect traditional customer survey data such as on demographics (74%) and attitude (54%)

• 85% of large corporations are now using social network accounts (e.g. brand accounts on Facebook, Twitter, Google+, Foursquare) as a marketing tool

• 65% of marketers said that comparing the effectiveness of marketing across different digital media is “a major challenge” for their business

• 37% of respondents did not include any mention of financial outcomes when asked to define what “marketing ROI” meant for their own organization

• 57% are not basing their marketing budgets on any ROI analysis

• 22% are using brand awareness as their sole measure to evaluate their marketing spend

In order to leverage the opportunities of big data, marketers need to improve their ability to:

• Collect meaningful customer data from a variety of sources, including real-time data

• Link that data to metrics developed for measuring marketing ROI

• Share data across the organization, linking datasets together at the customer level

• Utilize this shared data to effectively target and personalize marketing efforts to customers

In order to effectively harness the capabilities of new digital tools, marketers need to:

• Set clear business objectives for any digital marketing effort

• Develop a variety of metrics for new digital tools—from audience metrics, to engagement metrics, to financial metrics

• Develop models that link channel-specific digital metrics (like retweets or Facebook interactions) to universal metrics, including your key performance indicators (KPIs)

• Continuously innovate new measurement models, as new digital tools and marketing rapidly evolve

Above all, get started. Start with the basics of determining marketing ROI so you will create the largest impact on your organization:

• Make sure you’re using some kind of metrics on most of your marketing.

• Be ready to invest in getting some kind of data relevant to your measures.

• Make coordinating your traditional and digital media campaigns a goal.

• Set specific measurable objectives for all your campaigns.

• Put ROI in stated objectives for all your vendors (so they know your expectations to retain them or to cut them loose).

• Link marketing ROI to employee compensation, perhaps a bonus.

• Start today… as the pay off and learning curve will likely take a few years.

Then, move on to ROI best practices:

• Make sure your marketing metrics are accepted by finance.

• Make sure your data is: timely, actionable, linked at the customer-level, used to personalize marketing and target customers.

• Share your data across your organization.

Thanks to


COMPETING ON ANALYTICS: AN ARTICLE REVIEW

July 9, 2012

A Harvard Business Review Article by Thomas H. Davenport, Article Review by Akhmad Rahadian Hutomo

Since the late of 1990s, the term business intelligence (BI) and its application has been widely known and used in organizations, especially in large enterprises. But in a decade later, they started to realize that changing business environment will needs something more than just BI, which now called business analytics. In 2006, an author named Thomas wrote an article on HBR entitled “Competing on Analytics” which provisions the rising needs for business analytics. Davenport started his explanation on competing analytics by giving some examples on the successful usage of killer apps in some organizations, named Amazon, Harrah’s, Capital One and Boston Red Sox. By utilizing analytics, these organizations are able to knows better about the values that customer want, which inturn be able to squeeze all the value from the processes and make the best out of it. Davenport also point out that, to be an analytics competitor, top-down approach from the senior leadership team, as well as hiring the best people are necessary. Nonetheless, not all organizations are succesfull on using business analytics due to its characteristic. The rest of the articles explains about what organizations can make the best of analytics, as well as the changes that an organization must undergo to adopt it.

ANATOMY OF AN ANALYTICS COMPETITOR: MUST-HAVE CHARACTERISTICS FOR ORGANIZATIONS

Some traditional organizations may not be fully suitable with competing analytics. One best practice that an organization my want to know is how Marriot International using analytics. But, it will not work to some traditional organizations. Davenport’s study found three key attributes that an organization must have:

WIDESPREAD USE OF MODELLING AND OPTIMIZATION

Analytics competitors do things beyond statistics and spreadsheets. They are using sort of things that could provide them better insights from data, such as:

  • Predictive modelling to identify the most profitable customer.
  • Data warehouse to pool inhous and outside data.
  • Optimized supply chain.
  • Real-time pricing.
  • Sophisticated experiments to calculate impact.

Some analytics competitors, especially inscurance company, like Capital One and Progessive doing series comprehensive experiments to have the best value based on their customers need, even with high-risk.

AN ENTERPRISE APPROACH

Successfull analytics competitor will implement analytics using multiple applications in wide busines functions rather than using single app. For some companies such as UPS, Capital One and Barclays Bank are already implementing business intelligence and then shifting towards full-bore analytics competitors. However, Devenport thinks that BI still have some flaws where its still use data which spreads all over the organization. The data may contains errors and make the decision inacurrate, which in contrast, analytics competitors are using centralized function to manage critical data. People within the organization is as important as the technology. Some organization like P&G create a pool of experts from various function to do the analytics.

SENIOR EXCECUTIVE ADVOCATES

Changing into an analytics competitor simply changes the organization, and it will require leadership skills to guide the organization towards sucessfull adoption. Its proven that if the initiative just pushed by one-or-two business unit leaders, it will not successfull. There was some key leadership qualities that the article pointed out, such as: appreciation and familiarity with analytics or analytics-minded, intuitive, and have the guts to make decision even not supported by numbers.

THEIR SOURCES OF STRENGTH: WHAT MAKES AN ANALYTICS COMPETITOR RUNS

Basically Davenport define 4 things that makes an analytics competitor ticks, they are:

THE RIGHT FOCUS: HAVING A CLEAR SIGHT

Even if an organization have the ability, it is necessary to have certain focus on only a few analytics subjects. Becoming to diffuse can make the organization losing clear sight on the purpose of analytics. Another consideration of focus is about having a deep analysis on at least 7 functions. Nowadays, advanced statistic models and algorithm ca be used widely, including in advertising and other marketing measures. Later on this subtopic, there are examples that sucessfull analytics competitors can’t be done by the organization alone, it also needs to help their vendors and customers.

THE RIGHT CULTURE: TO JUSTIFY EVERYTHING QUICKLY

The right culture to have is the culture to appreciate usage of data, fact and the things between that and the procedure to get it. It also applied in organization with high creativity and intrapreneurship: any innovation should be made based on evidence. However, always justify everything also have payoff: it might be taking long time and costly, so the managers hould balance them in order to make quick decisions.

THE RIGHT PEOPLE: THE BEST OF THEM

Analytics competitors hires best people on analytics, bunch of them, to do the analytic-based decisions and make it seamlessly in line with the business. But, the people to do the analytics just as good as how far they can communicate it, so they must have sort of good interpersonal skills. In terms of formula, it might look like this:

Good Analyst = Expertise +Ablity to express it in simple way + Interpersonal skills

Of course, to get people with this quality is not easy, not to mention taking long waiting time. To have an overseas employee might be a good idea.

THE RIGHT TECHNOLOGY: THREE PILLARS

Analytics and IT are unseparable. It is supported by three pillars: First, THE DATA,whether it is from ERP, CRM, POS, any of them, and a lot of them, means years of data. They put it in data warehouse, which a familiar tools on BI. Second, THE BI SOFTWARE, to collect data from warehouses, analyse them and making reports. And Last, THE COMPUTING HARDWARE which enables a computation power for huge volume of data, quickly.

THE (LONG) ROAD AHEAD

Well, it might be not long road, as Davenport writhe the articles 5 years before this review written in the late 2011. He was concluding his paper with reminding us that to become an analytics competitor will takes a long time until the ROI, while meantime, it will cost many efforts and expenses. Yet, it can be done gradually from current time by collecting data and refining the system, and equip the organization with analytics-minded people.

COMMENTARY

Business analytics might be an interestring concept to explore to enrich our current knowledge and view on today’s business intelligence. In contrast with BI, business analytics focuses on gaining insights and overview of organizational performance based on data and statistical methods, supported by BI applications. It also cover the issues of leadership, culture and having a certain quality of analyst within the organization. On the article, Davenport gives the readers a comprehensive look of business analytics without losing the big picture. His writing also well supported with examples which gives personal and easy-to-digest touch on complex concept. A worth to read for BI enthusiasts.

Based on a Harvard Business Review Article Titled “Competing on Analytics” by Thomas H. Davenport Published on January 2006, Article Review By Akhmad Rahadian Hutomo for Business Intelligence Assignment, Information System, Faculty of Computer Science, Universitas Indonesia on October 2011.

http://ianhutomo.wordpress.com/2012/07/07/business-intelligence-in-human-capital-driven-companies/


The right BI tools for the job

July 2, 2012

I get the following question very often. What are the best practices for creating an enterprise reporting policy as to when to use what reporting tool/application?

Alas, as with everything else in business intelligence, the answer is not that easy. The old days of developers versus power users versus casual users are gone. The world is way more complex these days. In order to create such a policy, you need to consider the following dimensions:

Report/analysis type

  • Historical (what happened)
  • Operational (what is happening now)
  • Analytical (why did it happen)
  • Predictive (what might happen)
  • Prescriptive (what should I do about it)
  • Exploratory (what’s out there that I don’t know about)

Interaction types

  • Looking at static report output only
  • Lightly interacting with canned reports (sorting, filtering)
  • Fully interacting with canned reports (pivoting, drilling)
  • Assembling existing report, visualizations, and metrics into customized dashboards
  • Full report authoring capabilities

User types

  • Internal
  • External (customers, partners)

Data latency

Report latency, as in need the report:

  • Now
  • Tomorrow
  • In a few days
  • In a few weeks

Decision types

  • Strategic (a few complex decisions/reports per month)
  • Tactical (many less-complex decisions/reports per month)
  • Operational (many complex/simple decisions/reports per day)

Data sources

  • In an ideal situation (a single EDW, a single BI platform), this would not be relevant, but in most real situations, it is.

Self-produced versus IT-produced based on criteria such as:

  • Report complexity (number of joins, etc.)
  • Resulting report set size
  • Mission criticality of the report
  • External exposure
  • Level of operational risk
  • Individually used versus workgroup shared versus shared across department, LOB, enterprise

You will then need to come up with an 8-dimensional (or more) matrix (good luck :-) ), where at each intersection you need to indicate first and second choice for a specific BI tool/app best fit to address each use case. Did I miss any dimensions? Also, when and if you come up with something like this, or even as you are experimenting with prototypes, I’d love to see them and comment.

This blog originally appeared at Forrester Research.


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