Testing data-led targeting of low and mid productivity firms to increase awareness of performance and support

根据BE商业和特许人事与发展学会(CIPD)的证据，英国存在一个明显的问题，即企业高估了自己的相对生产率表现。我们认为，这种相对业绩意识是中小企业通过投资于技术和商业实践来提高生产率的主要障碍。在概念验证阶段，我们将通过以下方式演示这种方法所提供的可能性：\*改进现有的数据主导模型，该模型识别企业级别的生产力和特征，为瞄准中低生产率企业提供情报。*使用行为洞察(“推动理论”)和通过数据主导模型确定的企业级别的相对生产率数据，设计定制的营销产品。可以直接通过电子邮件发送给企业，以鼓励行为改变。\*通过小规模试验评估数据导向模式和定制营销的有效性，利用已确定的低生产率公司的样本并测试至少两种定制营销变体的点击率。概念验证的目的是为最具成本效益的方法添加强有力的证据：\*提高中低生产率企业对其相对业绩及其反映高生产率特征的程度的认识\*增加对实际(公共资金和非公共资金)支持的探索，以增加具有与高生产率企业相关特征的公司的数量。拟议的项目将寻求建立在利兹市地区已经开展的数据工作的基础上，以确定公司级别的生产率业绩和相关指标，并加强目前通过该城市地区的增长中心提供的业务支持服务。

"According to evidence from Be the Business and the Chartered Institute of Personnel and Development (CIPD), there is a clear issue in the UK of businesses overestimating their relative productivity performance. We believe this relative performance awareness is a primary barrier to SMEs increasing their productivity by investing in technology and business practices. This proof of concept will involve an initial small scale trial into the role that targeted data intelligence can play in encouraging businesses to address their low productivity performance and how this knowledge impacts on subsequent behaviour.In the proof of concept phase we will look to demonstrate the possibilities offered by this approach through:\*Refining the existing data-led model that identifies firm-level productivity and characteristics to provide intelligence for targeting low to mid productivity firms\*Designing a tailored marketing offer using behavioural insights (""nudge theory""), and firm-level relative productivity data identified through the data-led model, that can be emailed directly to businesses to encourage behaviour change.\*Evaluating the effectiveness of the data-led model and tailored marketing with a small scale trial, utilising a sample of the identified low productivity firms and testing click-through rates to a minimum of two variants of the tailored marketing.The aim of the proof of concept is to add robust evidence on the most cost-effective approaches to:\*Increase awareness amongst businesses with low to mid productivity of their relative performance and the extent to which they reflect high productivity characteristics\*Increase exploration of practical (publicly funded and non-publicly-funded) support to increase the number of firms with characteristics associated with high-productivity businesses.The proposed project will look to build on data work already being undertaken by the Leeds City Region to identify firm level productivity performance and related indicators, and to enhance the current business support offer delivered through the city region's Growth Hub."