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Can innovation in market research lead to winning messaging campaigns?

Newristics
02 MAY, 2022
Innovation in market research

Over the past decade, there has been a lot of innovation in the market research industry, changing the way insights professionals capture and report insights today. The industry has responded to the universal client need for making market research projects faster, cheaper and better.

1.DIY Survey Platforms – Market research surveys used to take weeks to develop/finalize and several more weeks to program for fielding. Survey programming required specialized software platforms like Confirmit, Sawtooth, etc. and could only be programmed by survey fieldwork experts.
Today, market research can be conducted through microsurveys that can be programmed directly by anyone on the insights team. Cloud-based customer feedback and intelligence platforms are making it easier for insights teams to test ideas, capture shopper behaviors, evaluate brand health, understand barriers to purchasing, and more.

2.First party customer databases – Historically, respondents for market research studies were recruited only through third party panel providers. In an attempt to get better respondent feedback while simultaneously also reducing panel costs, most large brands are now building first party customer databases of their own. By engaging customers (past or present) in short, intuitive surveys, brand teams can get higher quality feedback and insights from surveys.

3.Behavioral Science – Insights teams are replacing “stated” research techniques with behavioral science inspired methodologies to get more human insights from research. Behavioral science is a 3-time Nobel Prize winning field of research that has shed light on how humans make decisions using mental shortcuts like heuristics and biases.

Insights teams have embraced the use of behavioral science in research and are now accepting the fact that customers don’t DO what they SAY and often they don’t even say what they THINK or FEEL. Behavioral science helps bridge the gaps between FEEL/THINK/SAY/DO and helps brands teams get deeper, more actionable insights from every research project.

4.Data reporting and visualization – For decades, survey findings from market research projects were delivered through Powerpoint decks filled with boring bar charts and cross- tables. Innovation in data visualization technologies has made a big impact on how insights from market research studies are reported and shared within brand teams.

Real-time reporting of research data is also changing how brand teams act upon research findings. In the past, teams had to at least wait for the preliminary read out presentation, which took weeks to prepare from the last day of fieldwork. Today, real- time reporting of survey data through online dashboards makes it possible for insights teams to intervene early in research projects and get the right questions in the survey based on early data.

Lack of innovation in message testing research

Despite all the innovation seen in market research over the past decade, one type of market research that is still operating in the old model is message testing research. There has been little methodological innovation in message testing and standard survey methodologies conventionally used for message testing still don’t meet some basic needs of brand teams:

  1. Can’t test a lot - Researchers still cannot test a lot of messages in one survey. It limits the ability of brand teams from exploring a wide range of messaging ideas and platforms or researchers have to conduct rounds of iterative research to test a large number of messages.
  2. Sea of sameness – Conventional message testing methodologies often produce similar scores across all messages because they are not sensitive enough to pick up nuanced differences in messages. Marketing teams struggle to make campaign decisions if there isn’t enough separation between Good/Better/Best message scores from surveys and end up making decisions based on their judgment more than research.
  3. Message drivers of appeal – There is still no way for researchers to understand the driver of appeal for a message without asking stated diagnostic questions like, “What do you like/not like about this message?”
  4. Campaign readiness – The main deliverable of most conventional message testing methodologies is a rank order/hierarchy of messages, followed by a TURF type analysis to estimate the optimal number of messages to use. In order to be campaign ready, marketing teams need message testing research to ideally deliver a segment-level, channel specific message map, which is not what they are getting from research today.

With increasing use of digital marketing channels, message testing has also been shifting out of market research/insights teams to marketing teams because they are conducting in-market A/B testing as part of their marketing campaigns. While A/B testing is easy to conduct and uses real- world results, data shows that 80% of A/B tests don't produce any results and are canceled before they reach statistical significance.

A/B testing has many of the same constraints that market research-based message testing has. Most brand teams can only afford (time and money) to test a few (6-8) messages in A/B tests. A/B tests don’t explain why one option is better than the other and attribution analysis is difficult if the A/B tests are run on bundles of messages instead of single messages like email subject lines.

Future of message testing: Decision heuristics science + AI

Message testing research needs a reboot. Currently, it is not helping insights teams deliver what their marketing partners really need to execute winning messaging campaigns:

  • What insights teams deliver - message hierarchy and TURF analysis
  • What marketing teams need – Optimal messages bundles and storyflow, personalized for every segment and channel.

An ideal message testing system would require a fundamentally different methodological approach that uses science and algorithms to test more messages in market research and produces deliverables that directly lead to messaging campaigns.

In the past few years, latest advances in decision heuristics science and AI have created new opportunities to build a better message testing system. Decision heuristics science allows messaging choices to be set up as behavioral experiments in research. Artificial intelligence offers the ability to do more with research data and translate it into winning messaging campaigns.

Decision heuristics science can transform how messages are tested in surveys with respondents:

  • Test 100s of messages in one survey so that we can improve our chances of finding winning messaging campaigns
  • Get better data on each message so that we can find better message bundles out of billions of possibilities
  • Get true message appeal without asking so that we can create even more successful messaging campaigns in the future

Artificial Intelligence can transform how data from message testing surveys is used to drive campaigns:

  • Be more campaign ready straight out of the research by identifying optimal message bundles/storyflow for every channel
  • Reduce marketing waste by personalizing messaging campaigns for each customer segment based on their decision heuristics
  • Train predictive models so that we can refresh campaigns more often without more research

CMO (Choose Message Optimizer) from Newristics combines the power of decision heuristics science and AI to test messages in a better way. CMO has been proven superior in industries with the most complex messaging needs, like the pharmaceutical industry. In a meta-analysis of 50+ studies in the pharmaceutical industry, CMO was consistently proven to be superior:

  • 100% success rate – CMO-identified message bundles outperformed benchmarks/controls in ALL studies
  • 1.7x improvement – CMO-identified message bundles had 1.7x times higher preference share than ALL benchmarks
  • Competitive position – CMO-identified message bundles helped 7 out of 10 brands beat the market leader or extend their leadership over the nearest competitor.
Transforming message testing in banking industry

To demonstrate the value of innovation in message testing in the banking industry, Newristics conducted a large-scale, head-to-head, challenger type study using control messages from the #1 bank in the country - JP Morgan Chase. The study was designed to be the ultimate messaging challenge:

Can a different approach to developing and testing messages outperform the messaging efficacy of the country's largest bank...without the help of their marketing department?

Process

  • Collect marketing messages for a leading retail bank (50 messages selected for Chase)
  • Create alternative versions of Chase messages using decision heuristics science (350 new messages developed)
  • Test >400 messages with consumers (N=1,000) in a large-scale message testing survey using Choose Message Optimizer methodology.
  • Use AI to analyze survey data and identify winning message bundles from >2.5 trillion possibilities
  • Compare CMO-generated optimal message bundles to Chase in-market campaigns using Preference Share metric

Results

  • Using decision heuristics science and AI in message testing resulted in 100% success rate – every message bundle generated by CMO was superior to the in-market messaging campaign used by Chase.
  • New message bundles identified through CMO had 30%-50% higher preference share than Chase in-market messaging across all campaign types.

So, how was the bank messaging improved by 30-50%?

  • Increased and Improved Messaging Ideas - 350 new messages developed in 5 days using decision heuristics science
  • Turbo Message Testing - 400+ messages tested in only one survey with only 1,000 banking customers using a methodology powered by decision heuristics science
  • Optimal Channel & Campaign Execution - AI successfully identified winning message bundles for each channel out of 2.5 trillion possibilities.
  • Actionable Messaging Insights - Decision heuristics science identified precise language drivers of message appeal for different audience segments (e.g. Gen X vs Gen Z)

Since customer acquisition in banking is driven mostly by trigger events (e.g. moving to a new city), banks need an always-on messaging model so that they can be in the consideration set whenever a consumer is ready to switch. Being always-on is an expensive value proposition in messaging and can be cost prohibitive unless the messaging playbook is optimized for segment-level and channel-level messaging.

Retail banks have the opportunity to make their messaging campaigns significantly more effective by utilizing decision heuristics science + AI in the development and testing of messaging before the campaign launch. Download the full study report here and get up to speed on the latest scientific and algorithmic advances in messaging optimization.

About Newristics

Newristics is famous for helping brands optimize messaging using a combination of behavioral science and machine learning algorithms. In the past ten years, Newristics has optimized messaging for 100s of world-leading brands generating $100s billion in revenue every year.