Monthly Archives: March 2020

Growing Brand Trust is a Waste of Money

Опубликовано: 03/12/2020 в 11:57 am


Категории: Home Page Tiles,Think Page,Uncategorized

Brand trust is not a strong predictor of consumer behaviour or share of spend, nor does it prove your customers are emotionally committed to your brand. 

Consumers still buy from brands they distrust, provided they personally gain from the relationship. The more consumers gain from dealing with your brand than competitor brands, the less impact brand trust or distrust scores have on your revenue. 

5D, Australia’s leading strategic data consultancy, conducted comprehensive research into the nature of brand trust amongst 5,000 consumers, and found:

When asked if they trust a brand, consumers answer two different questions based on their ‘mindset’.

Two elements constitute trust – capability to do what you promise and ethical behaviour by being honest and doing no harm.

When consumers are asked if they trust a brand, 60% interpret the question as asking if the brand is capable to do what they promise, while 40% interpret the question as asking if the brand is ethical in how they operate.

A consumer’s mindset (capability vs ethics) has the biggest impact on whether or not they trust a brand, not what they hear about the brand in the media.

Customers with a Capability mindset are 30% more likely to say they trust brands, believing brands are capable of performing their basic functions. Current customers can more confidently answer how capable a brand is than non-customers, making them more likely to say they trust a brand, while non-customers who cannot answer the question are more likely to respond indifferent (neither trust or distrust the brand). 

While customers with an Ethics mindset are 88% more likely to say they distrust brands, despite being exposed to the same levels of negative brand media as those with a Capability mindset. This Ethics mindset leads to higher distrust for both customers and non-customers.

Consumers with an Ethics mindset are more likely to distrust multiple brands across multiple industries because they distrust brands in general. 

Brand trust and distrust are two different measures (capability vs ethics) and a net score of trust minus distrust is statistically meaningless.

Most consumers have no reason for trusting a brand, other than not having a reason to distrust the brand.

The weakness of measuring brand trust, compared to other consumer metrics such as customer satisfaction, is that 52% of customers are unable to give any reason for why they trust a brand. Brand trust cannot be validated by emotional commitment to the brand.

66% of customers who distrust a brand can provide evidence why the brand should not be trusted. That leaves around one third of all brand distrust being driven by distrusting brands in general, and not anything to do with the actual performance of your brand. 

You potentially know one of these hardened cynics who do not trust any brands in any industry. Some of them are your current customers.

Brand trust is a poor predictor of customer value relative to other customer metrics.

Brand trust is a weak predictor of brand consideration and future intent – trying to grow trust scores in the population is not an efficient way to increase revenue. The correlation between brand trust and brand consideration is only 0.48 – with no difference between customers and non-customers of brands. 

Brand trust does not guarantee the brand will be considered, 1 in 5 non-consumers who have a brand they prefer to all others claim they do not actually trust that brand – while of the brands most likely to never be considered 1 in 5 was highly trusted.

In fact, 70% of customers who distrust the brand they are with say they plan to either maintain or increase the number of products they have with that brand.

The reason for this incongruity is due to brand trust being most strongly correlated with brand awareness at 0.77. The more people are aware of your brand the more you are likely to receive higher brand trust scores in a survey, regardless of consumers intent to purchase from your brand. When non-customers have the same level of knowledge of a brand as current customers their brand trust levels are the same. 

The consequence is that, unlike several alternative customer metrics, increasing brand trust does not lead to a significantly greater share of consumer spend, because it does not strongly align to consumer behaviour. 

Spend patterns across multiple industries show that if a customer distrusts a brand they still spend on average 56% of their monthly spend with that brand. This only increases to 60% if they trust the brand.  Investing money trying to get existing customers to trust your brand brings a relative increase of only 7% of monthly spend. This is an extremely poor return on investment.

Many alternative customer metrics (like customer satisfaction) are much stronger predictors of customer behaviours, improving these metrics can result in more than a 50% increase in monthly spend, versus 7% for brand trust.

Trying to grow brand trust, that is automatically given to brands when people become more aware of the brand’s capabilities, is wasting your money. Focus on meeting the needs of your consumers better than competitors and identify alternative metrics that accurately predict consumer behaviours and where to invest to drive real growth.

Trust me.

CX Brain

Опубликовано: 03/12/2020 в 11:54 am


Категории: Home Page Tiles,Think Page

With all the investment and increased reliance on CX to drive growth, have you ever asked…

How did your company choose your Strategic CX Metric?

CX-Brain is an artificial intelligence tool that measures the efficacy of alternative CX metrics for your company.

There is no single leading CX metric that is right for every company and industry. We will identify the CX metric that optimises the link between your company strategy and consumer behaviours, to establish KPI’s that directly influence financial performance.

The Dirty Secret

Опубликовано: 03/12/2020 в 11:38 am


Категории: Home Page Tiles,Think Page

Accurate and representative data is the cornerstone of data led insights – it is the pillar on which any insights and analytics team builds its integrity and reputation.

Is your team building your strategies and reputation off accurate data?

In the digital age there is a greater chance of reporting fake data and risking your integrity as a source of truth. We must be confident in the data we source and ensure we are doing everything possible to collect and report accurate data.

Bad data cannot be fixed

  • You can’t increase its accuracy by increasing the sample size
  • All methods for collecting data have biases, no method is perfect
  • Weighting is not magic as not all biases are demographically driven

At 5D we regularly test the quality of the data we collect to ensure it is representative, and we only partner with proven reputable sources. 

In our latest data quality review we tested 8 research panels’ ability to match independent statistics of the Australian population we found:

  • The level of accuracy across the panels varied significantly (from 72% to only 45% across 20 key metrics) – despite each having the same identical sample profile weighted to be representative of the Australian population
  • Panels varied the most in their ability to accurately represent technology/media usage, lifestyle behaviours and socio-economic indicators
  • We also saw large differences in CX metric scores with some panels significantly deviating from the average scores for a range of brands

If you source quantitative data (especially for tracking studies) you need to test the quality of where you are sourcing your data from and if required blend your data sources so that you can manage sample bias. You should also monitor shifts in the panel quality over time.

You might find it is worth paying more to access quality data – when it comes to data, quality is definitely more important than quantity.

AI Is Listening

Опубликовано: 03/12/2020 в 11:29 am


Категории: Home Page Tiles,Think Page

Not all chat bots are the same, their design varies based on the core functions they are built to perform.

Have you met our chat bot Echo?

Echo is a chat bot we built within survey software – the advantage over pure chat bots is that you can seamlessly incorporate Echo’s functionality into any quantitative survey.

Echo is designed to elicit a greater depth of feedback to open ended questions in the same way an interviewer would do in a depth interview. In addition, Echo listens for missing information and can probe respondents on why they didn’t mention a theme as efficiently as why they did.

The advantage is a merging of rich qualitative and advanced quantitative feedback for each respondent, removing geographic constraints and optimising time and money.

Respondents are introduced to Echo and ‘trained’ to engage with Echo during the survey process. Visuals and dynamic text encourage more detailed responses while Echo leads the conversation into more specific questioning and themes.

The result is a greater depth of understanding of unstructured data that can be reported and coded immediately and an engaging survey process for the respondent.

CX Must Haves

Опубликовано: 03/12/2020 в 11:20 am


Категории: Think Page

Many companies start their VoC program with great enthusiasm, only to struggle with proving the ROI of the program investment a couple of years down the track. 

What are the must haves for a successful CX program?

We have been working with many of Australia’s largest companies on the design and management of their CX programs – often coming in when the program is failing to maintain relevance or drive change

Here are some of our recommended must haves for ensuring the long-term success of your CX program

1. Program Governance

Ensure strong leadership to focus the organisation on the importance of driving CX, forums to share results and the prioritising of accurate and robust measurement over results to remove gaming /  ensure honesty in the program. If results are poor own them, report them and deliver a plan to improve. 

2. Data Architecture 

Have a clear data architecture and metrics ecosystem for the linking of the Strategic, Relationship and Touchpoint CX programs.

3. Stakeholder Responsibility 

Ensure all metrics that are tracked are owned within the business, resist tracking metrics that are   statistically strong but strategically weak.

4. Customer Centricity 

Develop the ability/capacity to action negative customer feedback as efficiently as possible and use it to drive change within the business. If it is a known problem that has existed for years prioritise it over doing something new and exciting. Negative experience will undermine new initiatives every time.

5. Data Driven Decisions

Develop models that assist the business to invest in areas that will lead to the greatest return in CX and use them in decision making as opposed to opinion or politically led decisions.

6. Market Benchmarks 

Track the performance of the business and set goals relative to key competitors, especially new competitors that disrupt the market. Stay on top of how CX is evolving in your market and don’t become complacent. 

Confronting insights are the most powerful

Опубликовано: 03/12/2020 в 10:47 am


Категории: Home Page Tiles,Think Page

Customer centricity and mastering the integration of big data is key to business success, and this is dependent on efficiently identifying the most insightful customer feedback in the terabytes of data you collect. 

Research conducted by 5D shows some of the most powerful and insightful insights collected from voice of the customer programs contain profanity. Yet many people who run VoC programs deliberately delete comments containing profanity for no reason other than the comments might offend someone in the leadership team, and no one wants to offend the CEO.

Such language regularly offends your frontline staff.

Why shouldn’t the head of the company hear exactly what your frontline staff hear? Aren’t we all trying to get closer to our customers and understand their frustrations?

Historically the use of profanity has been viewed as an indicator of ‘norm violating behaviours’ such as being dishonest, criminal activity and anti-social behaviour. And therefore, we remove respondents who use foul language believing these people are more likely to have given dishonest and uninformative feedback.

A study on profanity conducted by Cambridge University psychologists in 2017 showed people who use profanity are more intelligent and honest than those who do not. The Cambridge University study was based on people’s Facebook posts where it is highly likely people are trying to appear intelligent or to entertain their friends when they use profanity to increase their social desirability.

5D used the principles of the Cambridge University study to measure the value of insights derived from customers who use profanity when providing feedback on products and services in a VoC program, and their power in informing business decision making. 5D analysed 80,000 customer comments from several Net Promoter Score (NPS) programs across a range of industries using multiple linguistic analyses to test the sophistication and depth of specificity of the language used (including the analysis used by Cambridge University) and found:

Customers who use profanity are just as intelligent and honest as customers who do not and are more likely to give considered responses. 

On average, customers who use profanity spend 15% more time providing feedback and use 3 times as many words to detail their answers in open text responses. If you remove customers who use profanity you are removing intelligent people who are providing honest and detailed feedback.

Customers use profanityas a genuine expression of emotion and this feedback is more likely to be authentic and unfiltered.

Profanity is mostly used to convey negative emotion. If you code profanity terms as indicators of the emotions, you can see there is a direct correlation between the use of profanity terms and lower NPS scores. When customers use profanity to convey anger their NPS score was -69, and when they use profanity to convey disgust their NPS score was -91.

Customers who use profanity are the unhappiest with your company’s products and services and removing their comments artificially biases your NPS results. 

The level of profanity increases (and therefore the NPS result decreases) in relationship (direct) NPS studies compared to strategic (blind) studies. Customers know the company they deal with is reading the feedback in a relationship study and they want you to know how unhappy they are. In a brand blind study there are fewer advantages to the customer venting their emotion or providing as detailed feedback as they do not know if anything constructive will result from their effort.

Do not remove customer comments that contain profanity from your VoC program, instead utilise these confronting insights;

  • Measure the use of profanity as an indicator of strong negative customer emotion – the negative emotion index can be tracked along with NPS or any other business metric as a companion metric
  • Target customers who use profanity to identify the issues that most frustrate your customers and drive negative word of mouth
  • Send comments containing profanity to the leadership team to demonstrate the depth of customer frustration and ensure feedback to the team that has the most power to drive improvements has not been sanitised

When developing your profanity code frame there are many ways customers can convey profanity including using acronyms and the asterisk – and profanity terms change with different cultures. 

In our study we identified 1,300 expressions of 34 core terms of profanity. 

Happy coding.